Negative energy balance:
The association between elevated milk fat to protein ratio in days
postpartum, pregnancy per AI to first service and postpartum
diseases in dairy cattle.
Research Project Large Animal Veterinary Medicine
October 2014
Student:
N.S. van de Burgwal (3382036), BSc, University of Utrecht
Supervisors:
Dr. K.N. Galvão, DVM, MPVM, PhD, DACT
Dr. P.L.A.M. Vos, DVM, PhD, Dilp ECAR & ECBHM, associate
professor
Table of contents
Abstract ......................................................................................................................................................... 4
1. Introduction ............................................................................................................................................... 5
2. Materials and Methods .............................................................................................................................. 7
2.1. Animals, housing, and diets................................................................................................................ 7
2.2. Milk composition ............................................................................................................................... 7
2.3. Definitions and diagnosis of postpartum diseases .............................................................................. 7
2.4. Reproduction ...................................................................................................................................... 7
2.5. Body condition score .......................................................................................................................... 8
2.6. Data handling ..................................................................................................................................... 8
2.6.1.Univariable analysis ..................................................................................................................... 8
2.6.2. Multivariable analysis ................................................................................................................. 8
3. Results ..................................................................................................................................................... 10
3.1. FPR in days ...................................................................................................................................... 10
3.2. Pregnancy loss .............................................................................................................................. 10
3.3. Pregnancy up to 300 DIM ............................................................................................................ 11
3.4. Postpartum diseases: DA, metritis, ketosis, indigestion and mastitis ............................................... 11
3.4.1. Displaced abomasum (DA) ....................................................................................................... 11
3.4.2. Metritis ...................................................................................................................................... 12
3.4.3. Ketosis ....................................................................................................................................... 13
3.4.4. Indigestion ................................................................................................................................. 14
3.4.5. Mastitis ...................................................................................................................................... 15
4. Discussion ............................................................................................................................................... 16
4.1. Association between FPR and pregnancy per AI to first service ..................................................... 16
4.1.1. Cut off........................................................................................................................................ 16
4.1.2. Time to first AI .......................................................................................................................... 17
4.2.1. Pregnancy loss ........................................................................................................................... 17
4.2.2. Time to pregnancy within 300 DIM .......................................................................................... 17
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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4.3. Association between postpartum diseases and FPR ........................................................................ 18
4.3.1. Metritis ...................................................................................................................................... 18
4.3.2. Ketosis ....................................................................................................................................... 18
4.3.3. Displaced Abomasum (DA) ...................................................................................................... 19
4.3.4. Indigestion ................................................................................................................................. 19
4.3.5. Mastitis ...................................................................................................................................... 19
5. Conclusion ............................................................................................................................................... 20
6. Acknowledgment..................................................................................................................................... 21
7. References ............................................................................................................................................... 22
8. Appendix ................................................................................................................................................. 27
Appendix A: Multivariable statistic analysis of P32AI and P74AI1....................................................... 27
Appendix B: Statistical procedures SAS P32AI1 and FPR1day. ............................................................ 29
Appendix D: Statistical procedures SAS postpartum diseases (DA, metritis, ketosis, indigestion,
mastitis) and FPR1day............................................................................................................................. 63
Appendix D: Kaplan-Meier survival curve time to pregnancy and FPR1day ....................................... 102
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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Abstract
The first objective of this study was to find a possible association between elevated milk fat to
protein ratio (FPR) in days postpartum and pregnancy per artificial insemination (AI) to first service. To
find a possible association between postpartum diseases and FPR in dairy cattle, was the second objective
of this study.
Data collected from 1200 Holstein Frisian milking cows were used in this study. Daily milk fat to
protein ratios were collected from each cow up to 21 days in milk (DIM) for the first objective. A
FPR>1.5 measured in more than 1,2,3,4 or 5 days within 21 DIM were collected. Pregnancy loss and time
to pregnancy within 300 DIM were collected. For the second objective, data was collected in the first 30
days after parturition for presence of the postpartum diseases metritis, ketosis, displaced abomasum (DA),
indigestion and mastitis.
In statistical analysis no significance was found for FPR in more than 1, 2, 3, 4 or 5 days within 21
DIM. For this reason, one or more days was chosen for further analysis since the number of cows were
most equally distributed between low and high FPR and pregnancy per AI to first service. Chi-square and
multiple logistic regression analysis were used to find a possible association between the variables.
No significant outcome between FPR>1.5 for one or more days during the first 21 DIM and
pregnancy to first AI. An association was found for the postpartum diseases DA, metritis and ketosis with
FPR>1.5. No significant association was found between mastitis and FPR>1.5, and indigestion and
FPR>1.5.
In this study FPR was not found to be a significant indicator to predict pregnancy per AI to first
service. Postpartum diseases are associated FPR and therefore can aggravate negative energy balance
(NEB) during early lactation.
Keywords: dairy cows, fat to protein ratio (FPR)>1.5, pregnancy per artificial insemination (AI) to first
service, postpartum diseases.
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1. Introduction
In the agriculture sector profit margins diminish. This is partly due to higher nutritional costs (Meulen et
al., 2011). As a consequence, the size of Dutch dairy farms increase and the number of dairy farms
decrease.
Dairy farmers prefer livestock with a high milk yield and relatively low nutritional costs. The
improvement of nutrition quality along with adaptation of dairy livestock genetics has increased the
average milk yield compared to twenty years ago (Leroy et al., 2008; Butler, 2003). The period between
calving is elongating due to lower conception rates at first artificial insemination (AI) (Butler, 2003). A
postponed pregnancy per AI to first service has proven to be less profitable for dairy farmers (Steeneveld
and Hogeveen, 2012).
The increase in average milk yield correlates with declining pregnancy rates per AI at first service
(See Figure 1; Butler, 2003). The dairy industry has been confronted with the negative effects from a
higher average milk yield. Cows producing high levels of milk exhibit less signs of heat and generally
have lower pregnancy rates per AI to first service. Not all literature is able to support this statement
(Santos et al., 2009).
Figure 1. An inverse association between conception rate (CR%) and annual milk production of HF cows in New York. First
service conception rate in 1951 of 65% declined to 40% in 1996 (Butler, 2003).
Reproductive failure at first AI can be assigned to the distorted energy balance of a cow. The fresh cow is
not able to consume enough energy to compensate the loss of energy by lactating. Besides during early
lactation, the cow looses more energy before parturition. Then, the energy state of fresh cows become a
negative energy balance (NEB) (Doepel et al., 2002; Bauman and Griinari, 2003). The increase in the
average milk yield indicates that cows need to consume more energy to make up for the energy loss due to
lactation. This will aggravate the NEB of the dairy cow since its capacity to obtain nutrition is limited.
Metabolic changes as a consequence of a NEB entail reproductive failure due to low quality
oocytes (Leroy et al., 2008). These metabolic changes start with the udder extracting glucose from the
blood to synthesize lactose. Then, the concentration of glucose in the blood will drop. This affects the
quality of prospective oocytes because glucose is necessary for ovarian follicles to develop. When glucose
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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is deficient DNA and RNA synthesis is disrupted in the ovarian follicles (Cetica et al., 2002; Sutton et al.,
2003).
Without sufficient glucose supply, the cow is obliged to employ other energy sources. Fat reserves
are addressed to substitute the lack of glucose. Fat is released from lipid cells and converted into ketone
bodies with use of non-esterified fatty acids (NEFAs). In the liver NEFAs turn into ketone bodies, which
substitute glucose as a source of energy. Another source of energy is protein. The urea level in the blood
of a cow increases as a negative side-effect of protein metabolism (Leroy et al., 2008). NEFAs, low
glucose levels, ketone bodies and high urea levels damage the cells of ovarian follicles. Research indicates
that NEFAs and ketone bodies (β-hydroxybutyrate) inhibit granulosa cell survival and proliferation (Leroy
et al., 2004; Van Holder et al., 2005).
Furthermore, research implies that embryonic quality is affected by NEB (Leroy et al., 2008).
Lactating cows with a NEB have less high quality embryos graded to be ‘excellent’ at 2-3 months after
parturition compared to non-lactating cows and beef cows. Embryos are graded excellent, good, fair, poor.
In which excellent embryos are spherical, symmetrical with cells of uniform size, color and texture. The
corresponding percentages are respectively 13.1%, 62.5% and 55.0% (Leroy et al., 2005a). Embryonic
cells of lactating cows with a NEB contain up to fifty percent more lipids, higher urea concentration and
darker cells compared to non-lactating and beef cows (Wiltbank et al., 2001; Sartori et al., 2002).
A NEB is manifested in the composition of milk in early lactation. Cows enduring a NEB tend to
have an increased fat concentration and a decreased protein concentration in their milk. This indicates that
milk fat to protein ratio (FPR) is a good indicator for a cow's NEB (Heuer et al., 1999; Duffield et al.,
1997). Because of the association between NEB and fertility (Leroy et al., 2008), FPR may be a good
predictor of pregnancy per artificial insemination (AI) to first service for dairy cattle if farmers are able to
collect daily milk data from individual cows. Advanced dairy farms have the equipment to measure the
milk composition.
Besides the imbalance in energy needed and consumed, postpartum diseases contribute to the
development of a cow's NEB as well (Toni et al., 2011; Heuer et al., 1999). The activated immune system
extracts energy from the cow in order to eliminate infectious diseases postpartum. Cows suffering from
non-infectious postpartum diseases show anorexic behaviour and diminishing intestinal absorption
(Collard et al., 2000). This will aggravate the declining energy intake (Drackley, 2006). This study
focuses on the infectious postpartum diseases metritis and mastitis, and the non-infectious postpartum
diseases displaced abomasum (DA), ketosis and indigestion.
The first objective of this study is to find a possible association between elevated milk fat to
protein ratio (FPR) in days postpartum and pregnancy per AI to first service. To find a possible
association between postpartum diseases and FPR in dairy cattle, is the second objective of this study. The
null hypothesis and alternative hypothesis of the first objective are the following:
H0: FPR has no effect on pregnancy per AI to first service.
H1: A FPR>1.5 is associated with lower pregnancy rates per AI to first service.
The null hypothesis and alternative hypothesis of the second objective are the following:
H0: Postpartum diseases are not associated with FPR.
H1: Postpartum diseases are associated with a FPR>1.5.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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2. Materials and Methods
2.1. Animals, housing, and diets
The data for this study were collected from cattle raised at The Dairy Research Unit. The Dairy Research
Unit is part of the animal science department of the University of Florida. The data were collected from
the 1189 Holstein Frisian dairy livestock (489 primiparous and 699 multiparous during 3 years (20102013). In 776 dairy cattle all data were completely available and used in the statistical analysis.
The dairy cows were housed in free stall barns and fed twice daily a total mixed ratio (TMR) diet.
2.2. Milk composition
Twice daily milk samples were collected up to 21 DIM from each cow. The milk samples were analysed
by AfiLab in AfiFarm Software (S.A.E. AFIKIM, Kibbutz, Afkim, Israel). The daily FPRs were
calculated from the collected milk samples.
2.3. Definitions and diagnosis of postpartum diseases
General health of each cow was assessed by farm personal and recorded on a daily basis in the AfiFarm
Dairy Records system. Routine physical examinations were performed at 4, 7, 12 DIM, according to
Standard Operating Procedures (SOP). During routine physical examinations the presence of ketone
bodies were estimated in each cow using a urine test strip (Ketostix®, Bayer). Cows having one or more
positive ketone body test results were considered being ketogenic.
Cows with 'dystocia' , 'twins', 'stillbirth' and/or 'retained placenta' ante- or postpartum were recorded as
having 'calving problems' (CalvProb). A cow in need of assistance when parturition was not proceeding
for half an hour was defined as dystocia. Cows still having fetal membranes 24 hours postpartum or longer
were recorded as having ‘retained placenta’.
Postpartum diseases displaced abosmasum (DA), metritis, indigestion and mastitis were assessed up to 30
DIM. Cows having a distinctive ping on left or right flank, with scant manure and were possibly anorexic
were suspected to have DA, which was operatively confirmed. Positively diagnosed metritis cows
contained the following criteria: abnormal rectal temperature, were depressed and had watery and fetid
uterine discharge. Cows with milk deviation, having diarrhea with hypomotile rumen contractions, and
with or without signs of being tympanic were recorded as having 'indigestion'. Cows with one or more
clinical signs of mastitis: changes to the consistency of quarter, painful on palpation of quarter, swollen
quarter, abnormal milk secretion, with or without abnormal rectal temperature, were recorded as
‘mastitis’ in Afifarm Dairy Record system. A down cow or a cow unsteady prior to calving to 1 or 2
days after calving without any other abnormal physical exam findings (especially mastitis or metritis)
was recorded to have 'milk fever'.
2.4. Reproduction
All cows were timed artificially inseminated (TAI) by farm personal. The fertility protocols that were used
were pre- and ovsynch (See Table 1). Transrectal ultrasonography was used to confirm pregnancy at 32 ±
3 days (P32AI1) and 74 ± 3 days (P74AI1) after first AI. Embryonic quality was determined using
‘pregnancy loss’ and ‘time to pregnancy within 300 DIM’. Pregnancy loss was defined as a positive
P32AI and a negative P74AI1 result. The time to pregnancy were recorded for all cows included in this
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study up to 300 DIM. Cows used for embryonic transfer at first service and cows with missing
reproductive data were excluded from the study.
Table 1. TAI protocol primiparous and multiparous cows. DIM = days in milk, PGF = prostaglandin, GnRH = gonadotropinreleasing hormone.
DIM
Treatment
Presynch 1
38-44
PGF
Presynch 2
52-58
PGF
Ovsynch, day 0
64-70
GnRH
Ovsynch, day 7
71-77
PGF
Ovsynch, day 9
80-86
GnRH
Ovsynch, day 10
81-87
Insemination
2.5. Body condition score
The BCS was scored prepartum (BCS0C) and at first AI (BCSAI1) using a five point scale with 0.25 point
increments (Wildman et al., 1982). Based on the score the cows were divided into three categories: ‘low’
(BCS<3.00), ‘med’ (BCS 3.00-3.25) and ‘high’ (BCS>3.25).
2.6. Data handling
Collected data stored at Afifarm Software were transferred to Microsoft Excel 2010 to be organized.
2.6.1.Univariable analysis
The cows were assigned to 5 FPR categories based on amount of days FPR>1.5 during the first 21 days
postpartum. A FPR greater than 1.5 has shown to be indicative of energy deficiency. More metabolic
postpartum diseases en higher rates were seen in cows with FPR>1.5 compared to lower cut offs (Heuer et
al., 1999; Duffield et al., 1997).
The FPR1day category contain cows with at least one day FPR>1.5. The FPR2 days category contains
cows with at least two days a FPR>1.5. This continues up to FPR5days. Univariable Chi-square analysis
using the program MedCalc were performed to determine statistical significance. Based on significant
association with pregnancy at first AI and/or most equally distribution of cows between the ‘yes’ and ‘no’
FPR(x)day(s) levels a category chosen to be used in the following statistical analysis in the study.
An univariable Chi-square analysis was used to determine statistical significance for pregnancy loss
between P32AI1 and P73AI1.
A Kaplan-Meier curve analysis was used in estimating a possible association between FPR and time to
pregnancy up to 300 DIM.
2.6.2. Multivariable analysis
Multivariable analysis were performed by multiple logistic regression analysis using the SAS 9.3
statistical program. Each postpartum disease (DA, ketosis, metritis, indigestion and mastitis) was used as
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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dependent variable in multiple logistic regression analysis. Variables used to find a possible association
with postpartum diseases were: BCS0C (‘high’ , ‘med’ , ‘low’), parity (‘prim’ or ‘multi’), FPR1day (‘yes’
or ‘no’), milkfever (‘yes’ or ‘no’), ketosis (‘yes’ or ‘no’) and CalvProb (‘yes’ or ‘no’).
Possible association with elevated FPR and postpartum diseases were also evaluated with multiple logistic
regression in SAS 9.3. Outcomes with a P-value ≤ 0.05 were considered statistically significant, and a
0.05 > P ≤ 0.10 was considered a tendency towards statistical difference.
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3. Results
3.1. FPR in days
Of all 776 cows, 43.04% (334/776) were pregnant and 56.96% (442/776) were not pregnant 32 days
after first AI. When a Chi-square analysis the performed, FPR1day, FPR2days, FPR3days, FPR4days and
FPR5days are statistically insignificant (See Table 2).
The prevalence of FPR1day ‘yes’ was 52.32% (406/776) and ‘no’ was 47.68% (370/776). The prevalence
of FPR5days ‘yes’ was 20.49% (159/776) and ‘no’ was 79.21% (617/776). With the increase in FPR days
being elevated the distribution between the FPR ‘yes’ and ‘no’ groups are disproportionate (See Table 2).
The most equal distributed category is FPR1day.
Table 2. Descriptive statistics and outcomes of the univariable Chi-square analysis. Odds ratio for pregnancy at first AI for 776
cows. Outcomes with a P-value ≤ 0.05 were considered statistically significant, and a 0.05 > P ≤ 0.10 was considered a tendency
towards statistical difference. OR = odd ratio, CI = confidence interval.
Variable
Comparison
FPR1day
No
Yes
FPR2days
No
Yes
FPR3days
No
Yes
FPR4days
No
Yes
FPR5days
No
Yes
Pregnant at 32 ±
3 DIM
42.97%
(159/370)
43.10%
(175/406)
42.80%
(202/472)
43.42%
(132/304)
43.87%
(236/538)
41.18% (98/238)
OR
95% CI
P-Value
1.01
0.76 – 1.34
0.97
Referent
-
-
1.03
0.77– 1.37
0.86
Referent
-
-
1.21
0.87 – 1.67
0.25
Referent
-
-
43.03%
(250/581)
43.08% (84/195)
1.00
0.72 – 1.39
0.99
Referent
-
-
42.95%
(265/617)
43.40% (69/159)
1.02
0.72 – 1.45
0.92
Referent
-
-
3.2. Pregnancy loss
All cows used in P32AI1 analysis, apart from one missing cow, were included into the Chi-square analysis
of pregnancy loss (See Table 3). In total 5.03% (39/775) pregnancies were lost between P32AI1 and
P74AI1. 3.78% (14/370) cows without FPR elevation within 21 DIM lost pregnancy. 6.17% (25/405)
cows with FPR elevation within 21DIM lost pregnancy. No statistical significant (p=0.13) effect of FPR
on pregnancy loss was found.
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Table 3. Descriptive statistics and outcomes of the univariable Chi-square analysis. Odds ratio for the resumption of pregnancy at
first AI for 775 cows. Outcomes with a P-value ≤ 0.05 were considered statistically significant, and a 0.05 > P ≤ 0.10 was
considered a tendency towards statistical difference.
Variable
Comparison
Pregnancy loss
OR
95% CI
P-value
FPR1day
No
3.78% (14/370)
1.67
0.86 – 3.27
0.13
Yes
6.17% (25/405)
Referent
-
-
3.3. Pregnancy up to 300 DIM
The prevalence of cows being not pregnant at 32 ± 3 days after first AI was 56.96% (442/776) (See Table
3). The time to pregnancy was recorded for cows with and without an elevated FPR until 21 DIM. No
statistical significant (P>0.05) effect of FPR on time to pregnancy up to 300 DIM was found (See Figure
2).
Figure 2. Kaplan-Meier survival curves for time to pregnancy up to 300 DIM for FPR>1.5 group (dashed line; n= 405) and
FPR<1.5 group (solid line; n= 370) in percentages. No statistical significant outcome was found (P>0.05).
3.4. Postpartum diseases: DA, metritis, ketosis, indigestion and mastitis
3.4.1. Displaced abomasum (DA)
The DA prevalence in the herd was 3.22% (25/776). When all cows (n=776) were included in the multiple
logistic regression analysis, FPR1day (p=0.01) and ketosis (p<.01) had a statistical significant effect on
DA (See Table 4). Cows with FPR>1.5 for one day within 21 DIM were 15.03 (95% CI: 1.99-113.80)
times more likely to develop DA up to 30 DIM than cows that did not have FPR >1.5 within 21 DIM 5.9%
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(24/406) vs. 0.27% (1/370); P=0.01. Cows with ketosis 8.53% (18/211) within 30 DIM were 1.55 times
more likely to develop DA within 30 DIM, compared to cows that did not have ketosis 0.12% (7/565).
Variables not considered significant with a p≥0.1 were eliminated from the multiple logistic regression by
a manual backward elimination were milk fever (p=0.79), calving problems (p=0.56), BCS at
parturition(p=0.23), parity (p=0.15).
Table 4. Descriptive statistics and outcomes of the multiple logistic regression of dependent variable displaced abomasum (DA).
The prevalence of DA up to 30 DIM in the herd (n=776) was 3.22% (n=25). Risk factors for DA up to 30 DIM were FPR1day
(p=0.01) and ketosis (p<.01). Insignificant factors were milk fever (p=0.79), calving problems (p=0.56), BCS at
parturition(p=0.23), parity (p=0.15).
DA = displaced abomasum, DIM = days in milk, OR=odd ratio, CI = confidence interval.
Variable
Comparison
FPR1day
Yes
5.91% (24/406)
15.03
1.99– 113.80
0.01
No
0.27% (1/370)
Referent
-
-
Yes
8.53% (18/211)
1.55
1.90 – 11.57
<.01
No
0.12% (7/565)
Referent
-
-
Ketosis
DA within 30 DIM
OR
95% CI
P-value
3.4.2. Metritis
The prevalence of metritis in the herd was 21.91% (n=170). When all cows (n=776) were included in the
multiple logistic regression analysis, FPR1day (p<.01), parity (p<.01), calving problems (p<.01) and
ketosis (p=0.02) had a statistical significant effect on metritis (See Table 5). Cows with FPR elevation
28.57% (116/406) within 21 DIM were 2.10 times more likely (p<.01, 95% CI: 1.42 – 3.10) to develop
metritis up to 30 DIM, than cows that did not have FPR elevation 14.59% (54/370) within 21 DIM.
Primiparous cows 32.29% (103/319) were 3.32 times more likely to develop metritis than multiparous
cows 14.66% (67/457).The incidence calving problems (CalvProb) in the herd 14.82% (115/776). 47.83%
(55/115) of the cows with calving problems had metritis. 17.40% (115/661) of the cows without calving
problems at parturition had metritis within 30 DIM. Cows with ketosis 26.45% (56/211) within 30 DIM
were 1.73 times more likely to develop metritis within 30 DIM, compared to cows that did not have
ketosis 20.18% (114/565).
Variables not considered significant with a p≥0.1 were eliminated from the multiple logistic regression by
a manual backward elimination were milk fever (p=0.55) and BCS at parturition (p=0.21).
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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Table 5. Descriptive statistics and outcomes of the multiple logistic regression of dependent variable metritis. The prevalence of
metritis up to 30 DIM in the herd was 21.91% (170/776). Risk factors for metritis up to 30 DIM were FPR1day (p<.01), parity
(p<.01), CalvProb (p<.01) and ketosis (p=0.02). Insignificant factors were milk fever (p=0.55) and BCS at parturition (p=0.21).
Prim = primiparous, Mult = multiparous, CalvProb = calving problems, DIM = days in milk, OR=odd ratio, CI = confidence
interval.
Variable
FPR1day
Parity
CalvProb
Ketosis
Comparison
Metritis within 30
DIM
OR
95% CI
P-value
Yes
28.57% (116/406)
2.10
1.42 – 3.10
<.01
No
14.59% (54/370)
Referent
-
-
Prim
32.29% (103/319)
3.32
2.24– 4.99
<.01
Mult
14.66% (67/457).
Referent
-
-
Yes
47.83% (55/115)
3.75
2.42 – 5.81
<.01
No
17.40% (115/661)
Referent
-
-
Yes
26.54% (56/211)
1.73
1.11 – 2.68
0.02
No
20.18% (114/565)
Referent
-
-
3.4.3. Ketosis
The prevalence of ketosis in the herd was 27.19% (211/776). When all cows (n=776) were included in the
multiple logistic regression analysis, FPR1day (p<.01) and parity (p<.01) had a statistical significant effect
on ketosis (See Table 6). Cows with FPR elevation 38.92% (158/406) within 21 DIM were 4.01 times
more likely (p<.01, 95% CI: 1.42 – 3.10) to develop ketosis within 12 DIM, than cows that did not have
FPR elevation 14.32% (53/370) within 21 DIM. Primiparous cows 12.23% (39/319) were less likely
(OR=0.20) to develop ketosis than multiparous cows 37.64% (172/457).
Variables not considered significant(P>0.1) were eliminated from the multiple logistic regression by a
manual backward elimination was milkfever (p=0.98). Variables with a 0.05>p≤0.2 were included in the
multivariable analysis were BCS0C: BCS0C ‘high’ to ‘low’(p=0.89), BCS0C ‘med’ to ‘low’(p=0.48) and
calving problems (p=0.06). The 'low' group consist of a total of 4 cows.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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Table 6. Descriptive statistics and outcomes of the multiple logistic regression of dependent variable ketosis. The prevalence of
ketosis up to 12 DIM in the herd was 27.19% (211/776). Risk factors for ketosis up to 30 DIM were FPR1day (p<.01) and parity
(p<.01). CalvProb had a tendency to being significant (p=0.06). Insignificant factors were milk fever (p=0.98), BCS0C high to
low (p=0.89), BCS0C med to low (p=0.48).
Prim = primiparous, Mult = multiparous, BCS0C = body condition score at parturition, CalvProb = calving problems, DIM =
days in milk, OR=odd ratio, CI = confidence interval.
*12 DIM (ketosis tested at 4,7 and 12 DIM).
Ketosis within *12
DIM
Variable
Comparison
FPR1day
Yes
38.92 (158/406)
4,01
2,78 – 5,86
<.01
No
14.32% (53/370)
Referent
-
-
Prim
12.23 (39/319)
0.20
0.14 – 0.31
<.01
Mult
37.64% (172/457)
Referent
-
-
High
31.11% (154/495)
0.86
0.11– 6.89
0.89
Low
50.00% (2/4)
Referent
-
-
Med
19.86% (55/277)
0.47
0.06– 3.79
0.48
Low
50.00% (2/4)
Referent
-
-
Yes
33.04% (38/115)
1.57
0.97 – 2.55
0.06
No
26.17% (173/661)
Referent
-
-
Parity
BCS0C
BCS0C
CalvProb
OR
95% CI
P-value
3.4.4. Indigestion
The prevalence of indigestion in the herd was 17.14% (133/776). When all cows (n=776) were included in
the multiple logistic regression analysis, parity (p<.01) and ketosis (p<.01 had a statistical significant
effect on indigestion (See Table 7). Primiparous cows 9.40% (30/319) were less likely to develop
indigestion than multiparous cows 22.54% (103/457). Cows with ketosis 29.86% (63/211) within 30 DIM
were 2.16 times more likely to develop indigestion within 30 DIM, compared to cows that did not have
indigestion 12.39% (70/565).
Variables not considered significant with a p≥0.1 were eliminated from the multiple logistic regression by
a manual backward elimination were milkfever (p=0.54), calving problems (p=0.12and BCS0C (0.13).
Variables with a 0.05>p≤0.2 were included in the multivariable analysis was FPR1day (p=0.07).
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
14
Table 7. Descriptive statistics and outcomes of the multiple logistic regression of dependent variable indigestion. The prevalence
of indigestion up to 30 DIM in the herd was 17.14% (133/776). Risk factors for indigestion up to 30 DIM were parity (p<.01) and
ketosis (p<.01). FPR1day (p=0.07) had a tendency to being significant. Insignificant factors were milk fever (p=0.54), calving
problems (p=0.12) and BCS0C (0.13).
Prim = primiparous, Mult = multiparous, BCS0C = body condition score at parturition,, DIM = days in milk, OR=odd ratio, CI =
confidence interval.
Indigestion within
30 DIM
Variable
Comparison
FPR1day
Yes
21.18% (86/406)
1.48
0.98 – 2.24
0.07
No
12.70% (47/370)
Referent
-
-
Prim
9.40% (30/319)
0.44
0.28 – 0.70
<.01
Mult
22.54% (103/457)
Referent
-
-
Yes
29.86% (63/211)
2.16
1.42 – 3.30
<.01
No
12.39% (70/565)
Referent
-
-
Parity
Ketosis
OR
95% CI
P-value
3.4.5. Mastitis
The prevalence of mastitis in the herd was 14.05% (109/776). When all cows (n=776) were included in the
multiple logistic regression analysis, no statistical significant effects on mastitis were found. Variables not
considered significant with a p≥0.1 were eliminated from the multiple logistic regression by a manual
backward elimination were milk fever (p=0.86), calving problems (p=0.60), ketosis (p=0.28), parity
(p=0.20), FPR1day (0.13).
Variables with a 0.05>p≤0.2 were included in the multivariable analysis was BCS0C: BCS0C ‘high’ to
‘low’(p=0.64) and BCS ‘med’ to ‘low’ (p=0.34
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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4. Discussion
The objectives of the study were 1) to find a possible association between elevated milk FPR in days
postpartum and pregnancy per AI to first service, 2) to find a possible association between postpartum
diseases (DA, metritis, ketosis, indigestion and mastitis) and FPR with the following variables included in
the multiple logistic regression analysis; body condition score antepartum (BCS0C), parity (Prim and
Mult), milk fever , ketosis and calving problems (CalvProb).
4.1. Association between FPR and pregnancy per AI to first service
In this study no significant association was found between FPR for one or more days during 21 DIM with
pregnancy per AI to first service.
Metabolic changes as a consequence of a NEB entail reproductive failure due to low quality
oocytes (Leroy et al., 2008). Multiple studies have found an association between NEB and inferior
oocytes (See Table 8; Leroy et al., 2008). It is assumed that FPR is an indirect parameter to measure NEB.
The common cut off used for FPR in literature is 1.5. A FPR greater than 1.5 has shown to be indicative of
energy deficiency. More metabolic postpartum diseases and higher rates were seen in cows with FPR>1.5
compared to lower cut offs (Heuer et al., 1999; Duffield et al., 1997). In contrast, Collard et al. (2000) had
not found any association between elevated FPR and pregnancy per AI to first service. It was suggested by
the authors that a lack of an association might have been caused by low milk yield in the herd. Lower milk
production could have lead to a decreased energy demand by the udder, and minimal NEB. A lower
energy demand could have made it possible for the cows to recover more quickly, what have lead to no
significant effect on pregnancy results per AI to first service. In this study milk yield was not included in
the data analysis.
Table 8. Survey of eight studies about oocyte quality in high yielding dairy cows (Leroy et al., 2008).
4.1.1. Cut off
In this study a cut off value of 1.5 for FPR was used to make a possible association with first insemination.
Toni et al. (2011) used more FPR cut offs to determine the most accurate cut off for the prediction of early
lactation health, milk production, and time to culling during lactation. FPR>2.0 appeared to be more
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
16
potent predictive value compared to lower FPR cut offs. This was based on regard to disease incidence,
the severe negative effect on milk production and herd survival that appeared to start at a FPR>2.0.
A follow-up of this study with different cut off values of FPR can be tested. Based on sensitivity and
specificity a cut-off value could be determined.
4.1.2. Time to first AI
All dairy livestock were inseminated by a synchronization protocol (See Table 1). Primiparous and
multiparous are first AI 81-87 DIM (See Table 1). The time between parturition and first AI is correlated
with pregnancy rate (Knelsen et al., 2005). The study of Ospina et al. (2010) it was also found that
conception rates improved when interval between parturition to first AI increased, at least up to 70 DIM.
Although, they also found a significant difference between cows with prepartum NEFA ≥0.27 mEq/L or
<0.27 mEq/L (P= 0.03). The cows with prepartum NEFA ≥0.27 mEq/L had lower conception rates up to
70 DIM compared to cows with prepartum NEFA <0.27 mEq/L. Relatively late first AI could influence
the effect of NEB on pregnancy per AI to first service. Dairy farms in The Netherlands seem to have
stabilized the pregnancy per AI to first service since 1995 around 55%. In contrast, pregnancy per AI has
been decreasing in the US (Butler, 2003). The stabilization in conception rate at first AI in The
Netherlands since 1995 was found to be due to a longer interval between parturition and first AI. In 1995
the conception success stabilized in The Netherlands with an average time to first AI started at
approximately 85 DIM (Knelsen et al., 2005). In the herd used in this study the average first AI was
around 81 DIM, which is comparable to the situation in 1995 in The Netherlands. The affected ovarian
follicles developed early postpartum are probably gone before first AI started in this herd.
4.2.1. Pregnancy loss
Small percentage of cows lost the pregnancy after first pregnancy confirmation at P32AI1. Progesterone is
a key hormone in continuing pregnancy. Progesterone is lower in cows that had a NEB after parturition,
up to three ovarian cycles after parturition. Progesterone is produced by the corpus luteum. Somehow the
corpus luteum is not capable to produce plenty of progesterone, after being affected by NEB, or due to
increased hepatic metabolism (Sartori et al., 2004). Besides progesterone, the embryo quality can be an
issue in cows exposed to a state of negative energy. Non-lactating cows have good quality of embryos
compared to lactating cows. In study of Leroy et al. (2005a) the quality of embryos at day 5 of conception,
at 2 up to 3 months after parturition, were found to be inferior and some even non-viable compared to
embryos of non-lactating cows or heifers (p<0.05). The milk production seem to have a significant role in
affecting embryo quality. Lactating cows use more energy compared to non-lactating or beef cattle. More
energy loss increases NEB. The NEB could be an explanatory factor in the differences between the groups
used in the study of Leroy et al. (2005a).
4.2.2. Time to pregnancy within 300 DIM
Like the results in P32AI1 and P74AI1 no association could be found in a possible delayed pregnancy and
FPR>1.5 within 300DIM (See Figure 2). If embryonic death due to NEB was increased, postponed
pregnancies would have occurred. Since pregnancy loss and P32AI1 were not significantly associated
with FPR>1.5, it was less likely for pregnancy within 300DIM to be associated with FPR>1.5.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
17
4.3. Association between postpartum diseases and FPR
The second objective in this study was to find a possible association between postpartum diseases (metritis,
ketosis, DA, indigestion and mastitis) and FPR. In general postpartum diseases are interrelated to NEB.
NEB may predispose to disease and postpartum diseases may aggravate the negative energy state
(Duffield et al., 1997; Heuer et al., 1999; Toni et al., 2011).
The postpartum diseases of infectious kind entail an increased energy demand and decreased
energy intake aggravate NEB. The increased demand for energy trough the activation of the immune
system. Infectious diseases lead to an up-regulation of metabolism and immune gene expression. In the
cells the mitochondria’s are being uncoupled, which further increases the energy loss. In addition to
increased demand the anorexic behaviour of the cow reduces energy intake (Drackley, 2006). Less energy
intake and a higher energy demand aggravates NEB.
A NEB can also contribute to the development of postpartum diseases. During NEB specific
components of the immune system are altered. This includes impaired neutrophyl function, lymphocyte
responsiveness to mitogen stimulation, antibody response and the production of cytokines by immune
cells (Lacetera et al., 2005; Wathes et al., 2009; Moyes et al., 2010). The down-regulation of these
immune components make the cow susceptible to infectious diseases.
Another physiological process is the protection of the fetus by depression of the maternal cell
mediated immunity. (Raghupathy, 1997). This low active immunological environment may result in
increased susceptibility to infection (Mehrzad et al., 2011).
4.3.1. Metritis
Metritis was strongly associated with FPR (P=0.0002). Like Ospina et al. (2010) and Dubuc et al. (2011)
high pre- or postpartum NEFAs are related to increase the risk of metritis.
In this study metritis is associated with calving problems (p<0.0001). Calving problems such as
retained placenta, dystocia, fetal maceration are predisposing factors for the development of metritis.
There is an open connection between uterus and the environment, which makes it easier for pathogens to
enter the uterus. It is inevitable for the uterus to get contaminated with bacteria. Periparturient the cow is
still in a state of immunosupression as mention earlier, which make it easier for bacteria to infect the
uterine wall (Esposito et al., 2014)
The variable parity was significantly associated with metritis (p<.0001), with primiparous cows
being more likely to have had metritis compared to multiparous cows. This is comparebale to the results
of Vergara et al. (2014) in which primiparous cows metritis rate was 22.6% versus multiparous cows with
a metritis rate of 14.1%. Perhaps more calving assistance was offered by farm personal, which increases
the risk for metritis. No statistical analysis was done between parity and calving problems. It is suggested
to include this analysis in further research.
4.3.2. Ketosis
The FPR has been suggested to be an indicator for NEB. A NEB can contribute to (sub)clinical ketosis in
dairy livestock (Duffield et al., 1997; Cenja and Chládek, 2005). Heuer et al. (1999) suggested that early
FPR>1.5 in milk to be a good indicator for lack of energy supply through nutritional intake. In this study
ketogenic livestock were also associated with FPR>1.5.
Although ketosis is associated with FPR>1.5, it was not significantly associated with pregnancy
per AI to first service. This corresponds with the result of FPR>1.5 not being associated with pregnancy at
first service AI. Ketosis is characterised in the blood by hyperketonemia and hypoglycemia. Due to
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
18
insufficient blood glucose concentrations other energy sources, such as fat are claimed. In the liver NEFAs
are being oxidized and converted in the krebscycle. Due to an overload of NEFAs being converted, the
Krebs cycle is obliged to divert acetyl coA into ketone bodies (Block, 2010).
A quick recovery from NEB could elucidate for the insignificant statistical association between
ketosis and pregnancy per AI to first service. The relatively long interval between parturition and first AI
may also have contributed to the lack of association between these variables.
4.3.3. Displaced Abomasum (DA)
Lactating cows that had developed DA postpartum have a strong significant association with FPR. In
several studies DA and NEB are being significantly associated (Cameron et al., 1998; Geishauser et al.,
1997).
The early postpartum period is considered to be the major risk period, because hypocalcemia,
metritis, NEB and nutritional factors play a central role in the pathogenesis of DA (Shaver, 1997). Nearly
50% of left DA cases are accompanied by NEB (Heuer, 2000). Geishauser et al. (1997) also found that
cases of left DA had higher odds to have lower milk yield, higher milk fat percentage, lower protein
percentage and a higher FPR.
4.3.4. Indigestion
In the study of Collard et al. (2000) more digestive and locomotive problems were associated with longer
and more extreme periods of NEB. Digestive and locomotory problems might interfere with the dry matter
intake of the cow. Digestive issues can compromise absorption, like diarrhea. Cows with locomotion
problems spend more time resting and less time spending on nutritional intake (Collard et al., 2000). The
decreased absorption and consumption could increase the negative energy state of the dairy cow.
The variable parity was also included in the statistical analysis and found to be significant in the
association with indigestion (P=0.0005). The primiparous cows were less likely to develop indigestion
was the outcome of the multivariable analysis. No reasonable explanation can be given for this outcome
due to limited research in this area.
4.3.5. Mastitis
Strangely, in this study no significant association could be made between mastitis and FPR or any other
variable. Cows that had a NEB are more likely to develop mastitis (Suriyasathaporn et al., 1999; Perkins
et al., 2002).
In contrary, Jayarao et al. (1999) looked at mastitis caused by Streptococces uberis in
investigating the effect of a NEB on the innate immune response. Cows were dietary NEB induced during
mid lactation. The effect of an induced NEB resulted to be minimal on immune function. Duffield et al.
(2009) and Toni et al. (2011) either had no significant relationship between FPR and clinical mastitis. No
reasonable explanation was given for these results.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
19
5. Conclusion
The first objective of this study was to find a possible association between elevated milk fat to protein
ratio (FPR) in days postpartum and pregnancy per artificial insemination (AI) to first service. The null
hypothesis and alternative hypothesis of this first objective are as followed:
H0: Fat to protein ratio (FPR) has no effect on pregnancy per artificial insemination (AI) to first service.
H1: A fat to protein ratio (FPR) >1.5 is associated with lower pregnancy rates per artificial insemination
(AI) to first service.
Previous studies have found an association between NEB and inferior oocytes (See Table 8; Leroy et al.,
2008). A NEB is reflected in the milk composition by a FPR>1.5 (Heuer et al., 1999; Duffield et al.,
1997). Although research suggest that higher FPR are associated with lower pregnancy rates per artificial
insemination (AI) to first service, this study has not found it. No significant association between FPR and
pregnancy was found (P>0.05). The null hypothesis has not been rejected. This means that the daily
monitoring of FPR in the first 21 days in lactation is not a good indicator for pregnancy at first AI.
The second objective of this study was to find a possible association between postpartum diseases (DA,
metritis, ketosis, indigestion and mastitis) and FPR in dairy cattle. The corresponding null hypothesis and
alternative hypothesis of this second objective are as followed:
H0: Postpartum diseases are not associated with a fat to protein ratio (FPR).
H1: Postpartum diseases associated with a fat to protein ratio FPR>1.5.
In the second objective there was a significant association found between DA (p=0.0087), metritis
(p=0.0002) and ketosis (P<.0001) and FPR>1.5. No significant association has been found between
indigestion (p=0.0654), mastitis (p=0.1295) and a FPR.
Therefore, the null hypothesis is rejected and the alternative hypothesis accepted between the postpartum
diseases DA, metritis, ketosis and FPR>1.5. Literature supports the significant associations for the
postpartum diseases DA, metritis and ketosis. The postpartum diseases are able to aggravate the negative
energy state (Duffield et al., 1997; Heuer et al., 1999; Toni et al., 2011). This aggravation of NEB is due
to increased energy demand and decreased energy intake when cows experience a postpartum disease
(Drackley et al., 2006).
The null hypothesis is not rejected for postpartum diseases indigestion and mastitis. In previous studies
mastitis was also found not to be associated with NEB (Jayarao et al., 1999; Duffield et al., 2009; Toni et
al., 2011). Collard et al. (2000) found digestive problems being associated with NEB. Besides the study
of Collard et al. (2000) no other research was found about the association between indigestion and NEB.
Additional research should be done to clarify these results.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
20
6. Acknowledgment
The author thanks the University of Florida and the staff for collecting the data from the Dairy Research
Unit (Gainesville, Fl), and special thanks to the abroad supervisor Dr. K.N. Galvão and national
supervisor Dr. P.L.A.M. Vos for their help in guiding the research project. I am especially grateful for the
help by Dr. K.N. Galvão and J.C.M. Vernooij in the statistical analysis of this study.
In this acknowledgment I also like to thank my colleague student H.M. Bosman and my sister M.H. van
de Burgwal for their support during the research project.
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21
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8. Appendix
Appendix A: Multivariable statistic analysis of P32AI and P74AI1.
Table 9. Descriptive statistics and outcomes of the multiple logistic regression of dependent variable P32AI1. In the herd
334(43.04%) were pregnant and 442(56.96%) not pregnant at P32AI1. Variables associated with P32AI1 are parity (p=0.0302),
BCSAI1 ‘high’ to ‘low’ (p=0.0071) and BCSAI1 ‘med’ to ‘low’ (p=0.0408). Insignificant factors were displaced abomasums
(0.9418), metritis (p=0.9170), mastitis (p=0.8747), ketosis (p=0.8268), calving problems (p=0.6772), BCS0C (p=0.6685),
FPR1day (0.6452), indigestion (p=3551), milkfever (p=0.3123).
BCSAI1 = body condition score at first AI, Mult = multiparous, Prim = primiparous, Med = medium, DIM = days in milk,
OR=odd ratio, CI = confidence interval.
Variable
Comparison
Pregnancy at 32 ± 3
days after first AI
OR
95% CI
P-value
Parity
Prim
49.21% (157/319)
1.390
1.032 – 1.873
0.0302
Mult
38.73% (177/457)
Referent
-
-
High
48.04% (147/306)
1.805
1.174 – 2.775
0.0071
Low
31.47% (45/143)
Referent
-
-
Med
43.43% (142/327)
1.552
1.019 – 2.365
0.0408
Low
31.47% (45/143)
Referent
-
-
BCSAI1
BCSAI1
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Table 10. Descriptive statistics and outcomes of the multiple logistic regression of dependent variable P74AI1. Variables
associated with P74AI1 is BCSAI1 ‘med’ to ‘low’ (p=0.0408). Parity (p=0.0644) and BCSAI1(p=0.1030) have a tendency to
being significant with P74AI1. Insignificant factors were mastitis (p=0.8969), metritis (p=0.8980), calving problems (0.8689),
FPR1day (0.6532), BCS0C (0.6382), ketosis (p=0.7223), milkfever (p=0.5392), displaced abomasums (p=0.4814), indigestion
(p=0.1909).
BCSAI1 = body condition score at first AI, Mult = multiparous, Prim = primiparous, Med = medium, DIM = days in milk,
OR=odd ratio, CI = confidence interval.
Variable
Comparison
Pregnancy at 74 ± 3
days after first AI
OR
95% CI
P-value
Parity
Prim
34.26% (138/319)
1.331
0.983 – 1.803
0.0644
Multi
34.21% (156/456)
Referent
-
-
High
43.28% (132/305)
1.785
1.153 – 2.796
0.0102
Low
27.97% (40/143)
Referent
-
-
Med
37.31% (122/327)
1.436
0.935 – 2.233
0.1030
Low
27.97% (40/143)
Referent
-
-
BCS38C
BCS38C
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Appendix B: Statistical procedures SAS P32AI1 and FPR1day.
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The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.NIENKESTUDY
P32AI1
2
binary logit
Fisher's scoring
Number of Observations Read
Number of Observations Used
777
776
Response Profile
Ordered
Value
1
2
P32AI1
Total
Frequency
1
0
334
442
Probability modeled is P32AI1='1'.
NOTE: 1 observation was deleted due to missing values for the response or explanatory variables.
Backward Elimination Procedure
Class Level Information
Design
Variables
Class
Value
Parity
Mult
Prim
0
1
BCS0C
high
low
med
1
0
0
0
0
1
BCS38C
high
low
med
1
0
0
0
0
1
CalvProb
0
1
0
1
Milkfever
0
1
0
1
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The SAS System
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The LOGISTIC Procedure
Class Level Information
Step
Design
Variables
Class
Value
Metritis
0
1
0
1
Mastitis
0
1
0
1
DA
0
1
0
1
Ketosis
0
1
0
1
Indigest
0
1
0
1
FatProt1d
0
1
0
1
0. The following effects were entered:
Intercept
Indigest
Parity
BCS0C
BCS38C
FatProt1d
CalvProb
Milkfever
Metritis
Mastitis
DA
Ketosis
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1062.685
1067.339
1060.685
1069.553
1134.711
1041.553
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The SAS System
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The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
19.1319
18.6613
18.1452
13
13
13
0.1191
0.1340
0.1521
Likelihood Ratio
Score
Wald
Step
1. Effect DA is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1062.685
1067.339
1060.685
1067.558
1128.062
1041.558
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
19.1266
18.6575
18.1423
12
12
12
0.0855
0.0971
0.1114
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.0053
1
0.9418
2. Effect Metritis is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
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The SAS System
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The LOGISTIC Procedure
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1062.685
1067.339
1060.685
1065.569
1121.419
1041.569
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
19.1157
18.6453
18.1294
11
11
11
0.0590
0.0678
0.0786
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.0162
2
0.9919
3. Effect Mastitis is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1062.685
1067.339
1060.685
1063.594
1114.789
1041.594
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The SAS System
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The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
19.0908
18.6193
18.1031
10
10
10
0.0391
0.0454
0.0532
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.0411
3
0.9978
4. Effect Ketosis is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1062.685
1067.339
1060.685
1061.642
1108.183
1041.642
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
19.0430
18.5736
18.0591
9
9
9
0.0248
0.0291
0.0345
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
0.0889
4
0.9990
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The SAS System
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The LOGISTIC Procedure
Step
5. Effect CalvProb is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1062.685
1067.339
1060.685
1059.815
1101.702
1041.815
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
18.8700
18.4028
17.8949
8
8
8
0.0156
0.0184
0.0220
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.2624
5
0.9983
6. Effect BCS0C is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
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The SAS System
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The LOGISTIC Procedure
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1062.685
1067.339
1060.685
1056.622
1089.202
1042.622
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
18.0621
17.6386
17.1893
6
6
6
0.0061
0.0072
0.0086
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
1.0690
7
0.9936
7. Effect FatProt1d is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1062.685
1067.339
1060.685
1054.835
1082.759
1042.835
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The SAS System
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The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
17.8500
17.4276
16.9837
5
5
5
0.0031
0.0038
0.0045
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
1.2813
8
0.9958
8. Effect Indigest is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1062.685
1067.339
1060.685
1053.696
1076.967
1043.696
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
16.9882
16.6144
16.2188
4
4
4
0.0019
0.0023
0.0027
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
2.1329
9
0.9892
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The SAS System
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The LOGISTIC Procedure
Step
9. Effect Milkfever is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1062.685
1067.339
1060.685
1052.804
1071.421
1044.804
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
15.8802
15.6335
15.3539
3
3
3
0.0012
0.0013
0.0015
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
3.1666
10
0.9772
NOTE: No (additional) effects met the 0.1 significance level for removal from the model.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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The SAS System
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The LOGISTIC Procedure
Summary of Backward Elimination
Step
1
2
3
4
5
6
7
8
9
Effect
Removed
DF
Number
In
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
2
1
1
1
10
9
8
7
6
5
4
3
2
0.0053
0.0109
0.0249
0.0479
0.1733
0.8054
0.2120
0.8551
1.0210
0.9418
0.9170
0.8747
0.8268
0.6772
0.6685
0.6452
0.3551
0.3123
DA
Metritis
Mastitis
Ketosis
CalvProb
BCS0C
FatProt1d
Indigest
Milkfever
Type 3 Analysis of Effects
Effect
DF
Wald
Chi-Square
Pr > ChiSq
Parity
BCS38C
1
2
4.6981
7.2605
0.0302
0.0265
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
Parity
Prim
BCS38C
high
BCS38C
med
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
-0.8409
0.3296
0.5906
0.4397
0.1829
0.1521
0.2195
0.2149
21.1360
4.6981
7.2417
4.1844
<.0001
0.0302
0.0071
0.0408
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
48.1
32.9
19.0
147628
Somers' D
Gamma
Tau-a
c
0.152
0.188
0.075
0.576
Odds Ratio Estimates and Profile-Likelihood Confidence Intervals
Effect
Parity Prim vs Mult
BCS38C high vs low
BCS38C med vs low
Unit
Estimate
1.0000
1.0000
1.0000
1.390
1.805
1.552
95% Confidence Limits
1.032
1.179
1.023
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1.874
2.791
2.379
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The SAS System
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The LOGISTIC Procedure
Odds Ratio Estimates and Wald Confidence Intervals
Effect
Parity Prim vs Mult
BCS38C high vs low
BCS38C med vs low
Unit
Estimate
1.0000
1.0000
1.0000
1.390
1.805
1.552
The SAS System
95% Confidence Limits
1.032
1.174
1.019
1.873
2.775
2.365
10:00 Thursday, August 7, 2014 313
The FREQ Procedure
Table of FatProt1d by P32AI1
FatProt1d
P32AI1
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
211 ‚
159 ‚
370
‚ 27.19 ‚ 20.49 ‚ 47.68
‚ 57.03 ‚ 42.97 ‚
‚ 47.74 ‚ 47.60 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
231 ‚
175 ‚
406
‚ 29.77 ‚ 22.55 ‚ 52.32
‚ 56.90 ‚ 43.10 ‚
‚ 52.26 ‚ 52.40 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
442
334
776
56.96
43.04
100.00
Frequency Missing = 1
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The SAS System
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The FREQ Procedure
Statistics for Table of FatProt1d by P32AI1
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
0.0013
0.9708
Likelihood Ratio Chi-Square
1
0.0013
0.9708
Continuity Adj. Chi-Square
1
0.0000
1.0000
Mantel-Haenszel Chi-Square
1
0.0013
0.9708
Phi Coefficient
0.0013
Contingency Coefficient
0.0013
Cramer's V
0.0013
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
211
Left-sided Pr <= F
0.5434
Right-sided Pr >= F
0.5144
Table Probability (P)
Two-sided Pr <= P
0.0578
1.0000
Effective Sample Size = 776
Frequency Missing = 1
Table of Parity by P32AI1
Parity
P32AI1
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Mult
‚
280 ‚
177 ‚
457
‚ 36.08 ‚ 22.81 ‚ 58.89
‚ 61.27 ‚ 38.73 ‚
‚ 63.35 ‚ 52.99 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Prim
‚
162 ‚
157 ‚
319
‚ 20.88 ‚ 20.23 ‚ 41.11
‚ 50.78 ‚ 49.22 ‚
‚ 36.65 ‚ 47.01 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
442
334
776
56.96
43.04
100.00
Frequency Missing = 1
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
40
The SAS System
10:00 Thursday, August 7, 2014 315
The FREQ Procedure
Statistics for Table of Parity by P32AI1
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
8.4251
0.0037
Likelihood Ratio Chi-Square
1
8.4136
0.0037
Continuity Adj. Chi-Square
1
8.0028
0.0047
Mantel-Haenszel Chi-Square
1
8.4142
0.0037
Phi Coefficient
0.1042
Contingency Coefficient
0.1036
Cramer's V
0.1042
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
280
Left-sided Pr <= F
0.9985
Right-sided Pr >= F
0.0023
Table Probability (P)
Two-sided Pr <= P
8.804E-04
0.0041
Effective Sample Size = 776
Frequency Missing = 1
The SAS System
10:00 Thursday, August 7, 2014 316
The FREQ Procedure
Table of BCS38C by P32AI1
BCS38C
P32AI1
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
high
‚
159 ‚
147 ‚
306
‚ 20.49 ‚ 18.94 ‚ 39.43
‚ 51.96 ‚ 48.04 ‚
‚ 35.97 ‚ 44.01 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
low
‚
98 ‚
45 ‚
143
‚ 12.63 ‚
5.80 ‚ 18.43
‚ 68.53 ‚ 31.47 ‚
‚ 22.17 ‚ 13.47 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
med
‚
185 ‚
142 ‚
327
‚ 23.84 ‚ 18.30 ‚ 42.14
‚ 56.57 ‚ 43.43 ‚
‚ 41.86 ‚ 42.51 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
442
334
776
56.96
43.04
100.00
Frequency Missing = 1
Statistics for Table of BCS38C by P32AI1
Statistic
DF
Value
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
Prob
41
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
2
10.9495
0.0042
Likelihood Ratio Chi-Square
2
11.1815
0.0037
Mantel-Haenszel Chi-Square
1
1.2695
0.2599
Phi Coefficient
0.1188
Contingency Coefficient
0.1180
Cramer's V
0.1188
Effective Sample Size = 776
Frequency Missing = 1
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
42
Appendix C: Statistical procedures SAS P74AI1/Ploss and FPR1day
The SAS System
10:00 Thursday, August 7, 2014 317
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.NIENKESTUDY
P74AI1
2
binary logit
Fisher's scoring
Number of Observations Read
Number of Observations Used
777
775
Response Profile
Ordered
Value
1
2
P74AI1
Total
Frequency
1
0
294
481
Probability modeled is P74AI1='1'.
NOTE: 2 observations were deleted due to missing values for the response or explanatory variables.
Backward Elimination Procedure
Class Level Information
Design
Variables
Class
Value
Parity
Mult
Prim
0
1
BCS0C
high
low
med
1
0
0
0
0
1
BCS38C
high
low
med
1
0
0
0
0
1
CalvProb
0
1
0
1
Milkfever
0
1
0
1
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
43
The SAS System
10:00 Thursday, August 7, 2014 318
The LOGISTIC Procedure
Class Level Information
Step
Design
Variables
Class
Value
Metritis
0
1
0
1
Mastitis
0
1
0
1
DA
0
1
0
1
Ketosis
0
1
0
1
Indigest
0
1
0
1
FatProt1d
0
1
0
1
0. The following effects were entered:
Intercept
Indigest
Parity
BCS0C
BCS38C
FatProt1d
CalvProb
Milkfever
Metritis
Mastitis
DA
Ketosis
Model Convergence Status
Quasi-complete separation of data points detected.
WARNING: The maximum likelihood estimate may not exist.
WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based
on the last maximum likelihood iteration. Validity of the model fit is questionable.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1030.808
1035.461
1028.808
1036.815
1101.955
1008.815
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
44
The SAS System
10:00 Thursday, August 7, 2014 319
The LOGISTIC Procedure
WARNING: The validity of the model fit is questionable.
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
19.9940
18.2076
15.5579
13
13
13
0.0954
0.1498
0.2738
Likelihood Ratio
Score
Wald
Step
1. Effect Mastitis is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
WARNING: The maximum likelihood estimate may not exist.
WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based
on the last maximum likelihood iteration. Validity of the model fit is questionable.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1030.808
1035.461
1028.808
1034.831
1095.318
1008.831
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
19.9772
18.1844
15.5424
12
12
12
0.0675
0.1102
0.2131
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.0168
1
0.8969
2. Effect Metritis is removed:
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
45
The SAS System
10:00 Thursday, August 7, 2014 320
The LOGISTIC Procedure
WARNING: The validity of the model fit is questionable.
Model Convergence Status
Quasi-complete separation of data points detected.
WARNING: The maximum likelihood estimate may not exist.
WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based
on the last maximum likelihood iteration. Validity of the model fit is questionable.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1030.808
1035.461
1028.808
1032.848
1088.682
1008.848
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
19.9608
18.1655
15.5266
11
11
11
0.0459
0.0778
0.1596
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.0332
2
0.9835
3. Effect CalvProb is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
WARNING: The maximum likelihood estimate may not exist.
WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based
on the last maximum likelihood iteration. Validity of the model fit is questionable.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
46
The SAS System
10:00 Thursday, August 7, 2014 321
The LOGISTIC Procedure
WARNING: The validity of the model fit is questionable.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1030.808
1035.461
1028.808
1030.875
1082.056
1008.875
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
19.9336
18.1430
15.5036
10
10
10
0.0299
0.0526
0.1148
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.0605
3
0.9961
4. Effect FatProt1d is removed:
Model Convergence Status
Quasi-complete separation of data points detected.
WARNING: The maximum likelihood estimate may not exist.
WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based
on the last maximum likelihood iteration. Validity of the model fit is questionable.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1030.808
1035.461
1028.808
1029.077
1075.605
1009.077
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
47
The SAS System
10:00 Thursday, August 7, 2014 322
The LOGISTIC Procedure
WARNING: The validity of the model fit is questionable.
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
19.7317
17.9389
15.3141
9
9
9
0.0196
0.0359
0.0827
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.2623
4
0.9921
5. Effect BCS0C is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1030.808
1035.461
1028.808
1028.651
1065.874
1012.651
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
16.1575
15.7902
15.4699
7
7
7
0.0237
0.0271
0.0304
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
2.8012
6
0.8333
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
48
The SAS System
10:00 Thursday, August 7, 2014 323
The LOGISTIC Procedure
WARNING: The validity of the model fit is questionable.
Step
6. Effect Ketosis is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1030.808
1035.461
1028.808
1026.777
1059.347
1012.777
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
16.0314
15.6643
15.3470
6
6
6
0.0136
0.0157
0.0177
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
2.9257
7
0.8918
7. Effect Milkfever is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
49
The SAS System
10:00 Thursday, August 7, 2014 324
The LOGISTIC Procedure
WARNING: The validity of the model fit is questionable.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1030.808
1035.461
1028.808
1025.174
1053.091
1013.174
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
15.6348
15.3444
15.0665
5
5
5
0.0080
0.0090
0.0101
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
3.2439
8
0.9181
8. Effect DA is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1030.808
1035.461
1028.808
1023.660
1046.925
1013.660
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
50
The SAS System
10:00 Thursday, August 7, 2014 325
The LOGISTIC Procedure
WARNING: The validity of the model fit is questionable.
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
15.1480
14.8651
14.5981
4
4
4
0.0044
0.0050
0.0056
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
3.7702
9
0.9259
9. Effect Indigest is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
AIC
SC
-2 Log L
1030.808
1035.461
1028.808
1023.405
1042.016
1015.405
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
13.4040
13.2021
12.9987
3
3
3
0.0038
0.0042
0.0046
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
5.3496
10
0.8666
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
51
The SAS System
10:00 Thursday, August 7, 2014 326
The LOGISTIC Procedure
WARNING: The validity of the model fit is questionable.
NOTE: No (additional) effects met the 0.1 significance level for removal from the model.
Summary of Backward Elimination
Step
1
2
3
4
5
6
7
8
9
Effect
Removed
DF
Number
In
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
2
1
1
1
1
10
9
8
7
6
5
4
3
2
0.0168
0.0164
0.0272
0.2019
0.8984
0.1263
0.3769
0.4957
1.7103
0.8969
0.8980
0.8689
0.6532
0.6382
0.7223
0.5392
0.4814
0.1909
Mastitis
Metritis
CalvProb
FatProt1d
BCS0C
Ketosis
Milkfever
DA
Indigest
Type 3 Analysis of Effects
Effect
DF
Wald
Chi-Square
Pr > ChiSq
Parity
BCS38C
1
2
3.4193
6.7310
0.0644
0.0345
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
Parity
Prim
BCS38C
high
BCS38C
med
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
-1.0002
0.2861
0.5792
0.3616
0.1891
0.1547
0.2255
0.2217
27.9843
3.4193
6.5970
2.6587
<.0001
0.0644
0.0102
0.1030
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
47.6
33.4
19.0
141414
Somers' D
Gamma
Tau-a
c
0.142
0.176
0.067
0.571
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
52
The SAS System
10:00 Thursday, August 7, 2014 327
The LOGISTIC Procedure
WARNING: The validity of the model fit is questionable.
Odds Ratio Estimates and Profile-Likelihood Confidence Intervals
Effect
Parity Prim vs Mult
BCS38C high vs low
BCS38C med vs low
Unit
Estimate
1.0000
1.0000
1.0000
1.331
1.785
1.436
95% Confidence Limits
0.983
1.153
0.935
1.803
2.796
2.233
Odds Ratio Estimates and Wald Confidence Intervals
Effect
Parity Prim vs Mult
BCS38C high vs low
BCS38C med vs low
Unit
Estimate
1.0000
1.0000
1.0000
1.331
1.785
1.436
95% Confidence Limits
0.983
1.147
0.930
The SAS System
1.803
2.776
2.217
09:07 Friday, August 29, 2014 151
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.NIENKESTUDY
Ploss
2
binary logit
Fisher's scoring
Number of Observations Read
Number of Observations Used
776
775
Response Profile
Ordered
Value
1
2
Ploss
Total
Frequency
1
0
39
736
Probability modeled is Ploss='1'.
NOTE: 1 observation was deleted due to missing values for the response or explanatory variables.
Backward Elimination Procedure
Class Level Information
low
med
0
0
Design
Variables
Class
Value
Parity
Mult
Prim
0
1
BCS38C
high
1
0
0
1
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
53
CalvProb
0
0
1
1
Metritis
0
1
0
1
Mastitis
0
1
0
1
Ketosis
0
0
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
54
The SAS System
09:07 Friday, August 29, 2014 152
The LOGISTIC Procedure
Class Level Information
Class
Indigest
0
Value
1
1
1
1
0
1
0
1
0
FatProt1d
Step
Design
Variables
0. The following effects were entered:
Intercept
Parity
BCS38C
FatProt1d
CalvProb
Metritis
Mastitis
Ketosis
Indigest
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
311.169
315.822
309.169
322.891
369.420
302.891
AIC
SC
-2 Log L
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
6.2777
6.2087
6.0778
9
9
9
Likelihood Ratio
Score
Wald
Step
Pr>ChiSq
0.7118
0.7189
0.7321
1. Effect Metritis is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
55
The SAS System
09:07 Friday, August 29, 2014 153
The LOGISTIC Procedure
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
311.169
315.822
309.169
320.892
362.767
302.892
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
6.2776
6.2087
6.0777
8
8
8
Likelihood Ratio
Score
Wald
Pr>ChiSq
0.6162
0.6239
0.6385
Residual Chi-Square Test
Step
Chi-Square
DF
0.0000
1
Pr>ChiSq
0.9967
2. Effect Mastitis is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
311.169
315.822
309.169
318.907
356.130
302.907
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
56
The SAS System
09:07 Friday, August 29, 2014 154
The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
6.2623
6.1946
6.0638
7
7
7
Likelihood Ratio
Score
Wald
Pr>ChiSq
0.5095
0.5172
0.5323
Residual Chi-Square Test
Step
Chi-Square
DF
0.0155
2
Pr>ChiSq
0.9923
3. Effect CalvProb is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
311.169
315.822
309.169
317.062
349.632
303.062
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
6.1068
6.0456
5.9196
6
6
6
Likelihood Ratio
Score
Wald
Pr>ChiSq
0.4113
0.4181
0.4323
Residual Chi-Square Test
Chi-Square
DF
0.1764
3
Pr>ChiSq
0.9813
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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The SAS System
09:07 Friday, August 29, 2014 155
The LOGISTIC Procedure
Step
4. Effect Indigest is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
311.169
315.822
309.169
315.567
343.484
303.567
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
5.6022
5.5761
5.4669
5
5
5
Likelihood Ratio
Score
Wald
Pr>ChiSq
0.3469
0.3497
0.3616
Residual Chi-Square Test
Step
Chi-Square
DF
0.7105
4
Pr>ChiSq
0.9500
5. Effect Parity is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
58
The SAS System
09:07 Friday, August 29, 2014 156
The LOGISTIC Procedure
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
311.169
315.822
309.169
313.989
337.253
303.989
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
5.1802
5.1199
5.0261
4
4
4
Likelihood Ratio
Score
Wald
Pr>ChiSq
0.2693
0.2752
0.2846
Residual Chi-Square Test
Step
Chi-Square
DF
1.1223
5
Pr>ChiSq
0.9521
6. Effect BCS38C is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
311.169
315.822
309.169
311.723
325.682
305.723
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
59
The SAS System
09:07 Friday, August 29, 2014 157
The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
3.4459
3.4324
3.3788
2
2
2
Likelihood Ratio
Score
Wald
Pr>ChiSq
0.1785
0.1798
0.1846
Residual Chi-Square Test
Step
Chi-Square
DF
2.8203
7
Pr>ChiSq
0.9011
7. Effect Ketosis is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
311.169
315.822
309.169
310.823
320.128
306.823
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
2.3466
2.3092
2.2657
1
1
1
Pr>ChiSq
0.1256
0.1286
0.1323
Residual Chi-Square Test
Chi-Square
DF
3.8490
8
Pr>ChiSq
0.8705
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
60
The SAS System
09:07 Friday, August 29, 2014 158
The LOGISTIC Procedure
Step
8. Effect FatProt1d is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
-2 Log L = 309.169
Residual Chi-Square Test
Chi-Square
DF
6.2087
9
Pr>ChiSq
0.7189
NOTE: All effects have been removed from the model.
Summary of Backward Elimination
Step
1
2
3
4
5
6
7
8
Effect
Removed
DF
Number
In
Wald
Chi-Square
1
1
1
1
1
2
1
1
7
6
5
4
3
2
1
0
0.0000
0.0155
0.1605
0.5292
0.4222
1.6772
1.0405
2.2657
Metritis
Mastitis
CalvProb
Indigest
Parity
BCS38C
Ketosis
FatProt1d
Pr>ChiSq
0.9967
0.9009
0.6887
0.4670
0.5158
0.4323
0.3077
0.1323
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Intercept
1
-2.9377
0.1643
319.6291
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
Pr>ChiSq
<.0001
61
The SAS System
09:07 Friday, August 29, 2014 159
The FREQ Procedure
Table of FatProt1d by Ploss
FatProt1d
Ploss
Frequency‚
Percent
‚
Row Pct
Col Pct
‚
‚0
0
‚
1
‚
‚1
‚
Total
356 ‚
14 ‚
370
380 ‚
25 ‚
405
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
‚ 45.94 ‚
1.81 ‚ 47.74
‚ 96.22 ‚
3.78 ‚
‚ 48.37 ‚ 35.90 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
‚ 49.03 ‚
3.23 ‚ 52.26
‚ 93.83 ‚
6.17 ‚
‚ 51.63 ‚ 64.10 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
736
94.97
39
5.03
775
100.00
Frequency Missing = 1
The SAS System
09:07 Friday, August 29, 2014 160
The FREQ Procedure
Statistics for Table of FatProt1d by Ploss
Statistic
DF
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
Likelihood Ratio Chi-Square
1
Continuity Adj. Chi-Square
1
Mantel-Haenszel Chi-Square
1
Phi Coefficient
Contingency Coefficient
Cramer's V
Value
Prob
2.3092
2.3466
1.8364
2.3063
0.0546
0.0545
0.0546
0.1286
0.1256
0.1754
0.1289
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
356
Left-sided Pr<= F
0.9549
Right-sided Pr>= F
0.0870
Table Probability (P)
Two-sided Pr<= P
0.0419
0.1411
Effective Sample Size = 775
Frequency Missing = 1
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
62
Appendix D: Statistical procedures SAS postpartum diseases (DA, metritis, ketosis,
indigestion, mastitis) and FPR1day.
The SAS System
09:07 Friday, August 29, 2014 229
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.NIENKESTUDY
DA
2
binary logit
Fisher's scoring
Number of Observations Read
Number of Observations Used
776
776
Response Profile
Ordered
Value
Total
Frequency
DA
1
2
1
0
25
751
Probability modeled is DA='1'.
Backward Elimination Procedure
Class Level Information
Design
Variables
Class
Value
Parity
Mult
Prim
0
1
BCS0C
high
low
med
1
0
0
CalvProb
0
1
0
1
Milkfever
0
1
0
1
Ketosis
0
1
0
1
FatProt1d
0
1
0
1
0
0
1
The SAS System
09:07 Friday, August 29, 2014 230
The LOGISTIC Procedure
Step
0. The following effects were entered:
Intercept
Parity
BCS0C
FatProt1d
CalvProb
Milkfever
Ketosis
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
63
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
222.950
227.604
220.950
193.884
231.117
177.884
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
43.0661
44.1400
24.9361
7
7
7
<.0001
<.0001
0.0008
Likelihood Ratio
Score
Wald
Step
1. Effect Milkfever is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
The SAS System
Intercept
Only
Intercept
and
Covariates
222.950
227.604
220.950
191.960
224.539
177.960
09:07 Friday, August 29, 2014 231
The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
42.9899
44.0522
24.8805
6
6
6
<.0001
<.0001
0.0004
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.0719
1
0.7886
2. Effect CalvProb is removed:
Model Convergence Status
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
64
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
222.950
227.604
220.950
190.282
218.207
178.282
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
42.6679
43.8854
24.7069
5
5
5
<.0001
<.0001
0.0002
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
0.4176
2
0.8116
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
65
The SAS System
09:07 Friday, August 29, 2014 232
The LOGISTIC Procedure
Step
3. Effect BCS0C is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
222.950
227.604
220.950
188.968
207.585
180.968
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
39.9817
38.0642
22.0035
3
3
3
<.0001
<.0001
<.0001
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
3.7290
4
0.4439
4. Effect Parity is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
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The SAS System
09:07 Friday, August 29, 2014 233
The LOGISTIC Procedure
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
222.950
227.604
220.950
189.295
203.258
183.295
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
37.6543
36.1504
20.6434
2
2
2
<.0001
<.0001
<.0001
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
6.0027
5
0.3060
NOTE: No (additional) effects met the 0.1 significance level for removal from the model.
Summary of Backward Elimination
Step
1
2
3
4
Effect
Removed
Milkfever
CalvProb
BCS0C
Parity
DF
Number
In
Wald
Chi-Square
Pr > ChiSq
1
1
2
1
5
4
3
2
0.0714
0.3380
2.9791
2.0481
0.7893
0.5610
0.2255
0.1524
Type 3 Analysis of Effects
Effect
FatProt1d
Ketosis
DF
Wald
Chi-Square
Pr > ChiSq
1
1
6.8778
11.2760
0.0087
0.0008
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
67
The SAS System
09:07 Friday, August 29, 2014 234
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
FatProt1d 1
Ketosis
1
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
-6.3334
2.7085
1.5459
1.0223
1.0328
0.4604
38.3846
6.8778
11.2760
<.0001
0.0087
0.0008
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
70.7
7.3
22.0
18775
Somers' D
Gamma
Tau-a
c
0.634
0.813
0.040
0.817
Odds Ratio Estimates and Profile-Likelihood Confidence Intervals
Effect
FatProt1d 1 vs 0
Ketosis
1 vs 0
Unit
Estimate
1.0000
1.0000
15.007
4.692
95% Confidence Limits
3.053
1.982
271.290
12.380
Odds Ratio Estimates and Wald Confidence Intervals
Effect
FatProt1d 1 vs 0
Ketosis
1 vs 0
The SAS System
Unit
Estimate
1.0000
1.0000
15.007
4.692
95% Confidence Limits
1.982
1.903
113.598
11.567
09:07 Friday, August 29, 2014 235
The FREQ Procedure
Table of FatProt1d by DA
FatProt1d
DA
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
369 ‚
1 ‚
370
‚ 47.55 ‚
0.13 ‚ 47.68
‚ 99.73 ‚
0.27 ‚
‚ 49.13 ‚
4.00 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
382 ‚
24 ‚
406
‚ 49.23 ‚
3.09 ‚ 52.32
‚ 94.09 ‚
5.91 ‚
‚ 50.87 ‚ 96.00 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
751
25
776
96.78
3.22
100.00
Statistics for Table of FatProt1d by DA
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
68
Chi-Square
Likelihood Ratio Chi-Square
Continuity Adj. Chi-Square
Mantel-Haenszel Chi-Square
Phi Coefficient
Contingency Coefficient
Cramer's V
1
1
1
1
19.7575
24.8145
17.9896
19.7320
0.1596
0.1576
0.1596
<.0001
<.0001
<.0001
<.0001
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
369
Left-sided Pr <= F
1.0000
Right-sided Pr >= F
1.623E-06
Table Probability (P)
Two-sided Pr <= P
1.559E-06
1.801E-06
Sample Size = 776
The SAS System
09:07 Friday, August 29, 2014 236
The FREQ Procedure
Table of Ketosis by DA
Ketosis
DA
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
558 ‚
7 ‚
565
‚ 71.91 ‚
0.90 ‚ 72.81
‚ 98.76 ‚
1.24 ‚
‚ 74.30 ‚ 28.00 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
193 ‚
18 ‚
211
‚ 24.87 ‚
2.32 ‚ 27.19
‚ 91.47 ‚
8.53 ‚
‚ 25.70 ‚ 72.00 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
751
25
776
96.78
3.22
100.00
Statistics for Table of Ketosis by DA
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
26.1993
<.0001
Likelihood Ratio Chi-Square
1
22.5316
<.0001
Continuity Adj. Chi-Square
1
23.9128
<.0001
Mantel-Haenszel Chi-Square
1
26.1656
<.0001
Phi Coefficient
0.1837
Contingency Coefficient
0.1807
Cramer's V
0.1837
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
558
Left-sided Pr <= F
1.0000
Right-sided Pr >= F
2.653E-06
Table Probability (P)
Two-sided Pr <= P
2.324E-06
2.653E-06
Sample Size = 776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
69
The SAS System
09:07 Friday, August 29, 2014 191
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.NIENKESTUDY
Metritis
2
binary logit
Fisher's scoring
Number of Observations Read
Number of Observations Used
776
776
Response Profile
Ordered
Value
1
2
Metritis
Total
Frequency
1
0
170
606
Probability modeled is Metritis='1'.
Backward Elimination Procedure
Class Level Information
Design
Variables
Class
Value
Parity
Mult
Prim
0
1
BCS0C
high
low
med
1
0
0
CalvProb
0
1
0
1
Milkfever
0
1
0
1
Ketosis
0
1
0
1
FatProt1d
0
1
0
1
0
0
1
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
70
The SAS System
09:07 Friday, August 29, 2014 192
The LOGISTIC Procedure
Step
0. The following effects were entered:
Intercept
Parity
BCS0C
FatProt1d
CalvProb
Milkfever
Ketosis
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
817.935
822.589
815.935
728.133
765.366
712.133
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Step
Chi-Square
DF
Pr > ChiSq
103.8017
104.9035
87.2812
7
7
7
<.0001
<.0001
<.0001
1. Effect Milkfever is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
817.935
822.589
815.935
726.471
759.050
712.471
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
71
The SAS System
09:07 Friday, August 29, 2014 193
The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
103.4639
104.6906
87.0002
6
6
6
<.0001
<.0001
<.0001
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.3506
1
0.5538
2. Effect BCS0C is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
817.935
822.589
815.935
725.474
748.745
715.474
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
100.4609
101.9135
84.9569
4
4
4
<.0001
<.0001
<.0001
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
3.7833
3
0.2858
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
72
The SAS System
09:07 Friday, August 29, 2014 194
The LOGISTIC Procedure
NOTE: No (additional) effects met the 0.1 significance level for removal from the model.
Summary of Backward Elimination
Effect
Removed
Step
1
2
DF
Number
In
Wald
Chi-Square
Pr > ChiSq
1
2
5
4
0.3486
3.1162
0.5549
0.2105
Milkfever
BCS0C
Type 3 Analysis of Effects
Effect
DF
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
34.4611
14.3584
34.9781
5.8982
<.0001
0.0002
<.0001
0.0152
Parity
FatProt1d
CalvProb
Ketosis
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
Parity
FatProt1d
CalvProb
Ketosis
Prim
1
1
1
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
-2.7095
1.1993
0.7576
1.3209
0.5450
0.2175
0.2043
0.1999
0.2233
0.2244
155.2170
34.4611
14.3584
34.9781
5.8982
<.0001
<.0001
0.0002
<.0001
0.0152
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
69.3
21.0
9.7
103020
Somers' D
Gamma
Tau-a
c
0.484
0.535
0.166
0.742
Odds Ratio Estimates and Profile-Likelihood Confidence Intervals
Effect
Parity
FatProt1d
CalvProb
Ketosis
Prim
1 vs
1 vs
1 vs
vs Mult
0
0
0
Unit
Estimate
1.0000
1.0000
1.0000
1.0000
3.318
2.133
3.747
1.725
95% Confidence Limits
2.235
1.447
2.417
1.111
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
4.985
3.173
5.811
2.681
73
The SAS System
09:07 Friday, August 29, 2014 195
The LOGISTIC Procedure
Odds Ratio Estimates and Wald Confidence Intervals
Effect
Parity
FatProt1d
CalvProb
Ketosis
Prim
1 vs
1 vs
1 vs
vs Mult
0
0
0
Unit
Estimate
1.0000
1.0000
1.0000
1.0000
3.318
2.133
3.747
1.725
The SAS System
95% Confidence Limits
2.223
1.442
2.418
1.111
4.952
3.157
5.804
2.677
09:07 Friday, August 29, 2014 196
The FREQ Procedure
Table of FatProt1d by Metritis
FatProt1d
Metritis
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
316 ‚
54 ‚
370
‚ 40.72 ‚
6.96 ‚ 47.68
‚ 85.41 ‚ 14.59 ‚
‚ 52.15 ‚ 31.76 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
290 ‚
116 ‚
406
‚ 37.37 ‚ 14.95 ‚ 52.32
‚ 71.43 ‚ 28.57 ‚
‚ 47.85 ‚ 68.24 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
606
170
776
78.09
21.91
100.00
Statistics for Table of FatProt1d by Metritis
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
22.1047
<.0001
Likelihood Ratio Chi-Square
1
22.5869
<.0001
Continuity Adj. Chi-Square
1
21.2953
<.0001
Mantel-Haenszel Chi-Square
1
22.0763
<.0001
Phi Coefficient
0.1688
Contingency Coefficient
0.1664
Cramer's V
0.1688
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
316
Left-sided Pr <= F
1.0000
Right-sided Pr >= F
1.574E-06
Table Probability (P)
Two-sided Pr <= P
9.244E-07
2.432E-06
Sample Size = 776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
74
The SAS System
09:07 Friday, August 29, 2014 197
The FREQ Procedure
Table of Parity by Metritis
Parity
Metritis
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Mult
‚
390 ‚
67 ‚
457
‚ 50.26 ‚
8.63 ‚ 58.89
‚ 85.34 ‚ 14.66 ‚
‚ 64.36 ‚ 39.41 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Prim
‚
216 ‚
103 ‚
319
‚ 27.84 ‚ 13.27 ‚ 41.11
‚ 67.71 ‚ 32.29 ‚
‚ 35.64 ‚ 60.59 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
606
170
776
78.09
21.91
100.00
Statistics for Table of Parity by Metritis
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
34.1218
<.0001
Likelihood Ratio Chi-Square
1
33.6799
<.0001
Continuity Adj. Chi-Square
1
33.0992
<.0001
Mantel-Haenszel Chi-Square
1
34.0778
<.0001
Phi Coefficient
0.2097
Contingency Coefficient
0.2052
Cramer's V
0.2097
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
390
Left-sided Pr <= F
1.0000
Right-sided Pr >= F
5.433E-09
Table Probability (P)
Two-sided Pr <= P
3.531E-09
7.468E-09
Sample Size = 776
The SAS System
09:07 Friday, August 29, 2014 169
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.NIENKESTUDY
Ketosis
2
binary logit
Fisher's scoring
Number of Observations Read
Number of Observations Used
776
776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
75
Response Profile
Ordered
Value
1
2
Ketosis
Total
Frequency
1
0
211
565
Probability modeled is Ketosis='1'.
Backward Elimination Procedure
Class Level Information
Step
Design
Variables
Class
Value
Parity
Mult
Prim
0
1
BCS0C
high
low
med
1
0
0
CalvProb
0
1
0
1
Milkfever
0
1
0
1
FatProt1d
0
1
0
1
0
0
1
0. The following effects were entered:
Intercept
Parity
BCS0C
FatProt1d
CalvProb
Milkfever
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
76
The SAS System
09:07 Friday, August 29, 2014 170
The LOGISTIC Procedure
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
910.148
914.802
908.148
779.764
812.343
765.764
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Step
Chi-Square
DF
Pr > ChiSq
142.3840
127.9888
109.4773
6
6
6
<.0001
<.0001
<.0001
1. Effect Milkfever is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
910.148
914.802
908.148
777.764
805.689
765.764
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
77
The SAS System
09:07 Friday, August 29, 2014 171
The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
142.3835
127.9156
109.4863
5
5
5
<.0001
<.0001
<.0001
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
0.0004
1
0.9838
NOTE: No (additional) effects met the 0.1 significance level for removal from the model.
Summary of Backward Elimination
Step
1
Effect
Removed
Milkfever
DF
Number
In
Wald
Chi-Square
Pr > ChiSq
1
4
0.0004
0.9838
Type 3 Analysis of Effects
Effect
Parity
BCS0C
FatProt1d
CalvProb
DF
Wald
Chi-Square
Pr > ChiSq
1
2
1
1
58.5503
9.9136
53.1809
3.4085
<.0001
0.0070
<.0001
0.0649
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
Parity
BCS0C
BCS0C
FatProt1d
CalvProb
Prim
high
med
1
1
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
1
-1.0200
-1.5904
-0.1515
-0.7625
1.3875
0.4531
1.0706
0.2078
1.0619
1.0689
0.1903
0.2454
0.9077
58.5503
0.0203
0.5088
53.1809
3.4085
0.3407
<.0001
0.8866
0.4756
<.0001
0.0649
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
78
The SAS System
09:07 Friday, August 29, 2014 172
The LOGISTIC Procedure
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
71.5
19.3
9.2
119215
Somers' D
Gamma
Tau-a
c
0.523
0.576
0.207
0.761
Odds Ratio Estimates and Profile-Likelihood Confidence Intervals
Effect
Parity
BCS0C
BCS0C
FatProt1d
CalvProb
Prim
high
med
1 vs
1 vs
vs Mult
vs low
vs low
0
0
Unit
Estimate
1.0000
1.0000
1.0000
1.0000
1.0000
0.204
0.859
0.466
4.005
1.573
95% Confidence Limits
0.134
0.092
0.049
2.775
0.968
0.303
7.779
4.270
5.855
2.539
Odds Ratio Estimates and Wald Confidence Intervals
Effect
Parity
BCS0C
BCS0C
FatProt1d
CalvProb
Prim
high
med
1 vs
1 vs
vs Mult
vs low
vs low
0
0
Unit
Estimate
1.0000
1.0000
1.0000
1.0000
1.0000
0.204
0.859
0.466
4.005
1.573
95% Confidence Limits
0.136
0.107
0.057
2.758
0.972
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
0.306
6.889
3.791
5.815
2.545
79
The SAS System
09:07 Friday, August 29, 2014 173
The FREQ Procedure
Table of Parity by Ketosis
Parity
Ketosis
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Mult
‚
285 ‚
172 ‚
457
‚ 36.73 ‚ 22.16 ‚ 58.89
‚ 62.36 ‚ 37.64 ‚
‚ 50.44 ‚ 81.52 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Prim
‚
280 ‚
39 ‚
319
‚ 36.08 ‚
5.03 ‚ 41.11
‚ 87.77 ‚ 12.23 ‚
‚ 49.56 ‚ 18.48 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
565
211
776
72.81
27.19
100.00
Statistics for Table of Parity by Ketosis
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
61.2750
<.0001
Likelihood Ratio Chi-Square
1
65.8919
<.0001
Continuity Adj. Chi-Square
1
59.9981
<.0001
Mantel-Haenszel Chi-Square
1
61.1960
<.0001
Phi Coefficient
-0.2810
Contingency Coefficient
0.2705
Cramer's V
-0.2810
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
285
Left-sided Pr <= F
5.174E-16
Right-sided Pr >= F
1.0000
Table Probability (P)
Two-sided Pr <= P
4.004E-16
7.234E-16
Sample Size = 776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
80
The SAS System
09:07 Friday, August 29, 2014 174
The FREQ Procedure
Table of BCS0C by Ketosis
BCS0C
Ketosis
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
high
‚
341 ‚
154 ‚
495
‚ 43.94 ‚ 19.85 ‚ 63.79
‚ 68.89 ‚ 31.11 ‚
‚ 60.35 ‚ 72.99 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
low
‚
2 ‚
2 ‚
4
‚
0.26 ‚
0.26 ‚
0.52
‚ 50.00 ‚ 50.00 ‚
‚
0.35 ‚
0.95 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
med
‚
222 ‚
55 ‚
277
‚ 28.61 ‚
7.09 ‚ 35.70
‚ 80.14 ‚ 19.86 ‚
‚ 39.29 ‚ 26.07 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
565
211
776
72.81
27.19
100.00
Statistics for Table of BCS0C by Ketosis
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
2
12.4222
0.0020
Likelihood Ratio Chi-Square
2
12.7051
0.0017
Mantel-Haenszel Chi-Square
1
11.2001
0.0008
Phi Coefficient
0.1265
Contingency Coefficient
0.1255
Cramer's V
0.1265
WARNING: 33% of the cells have expected counts less
than 5. Chi-Square may not be a valid test.
Sample Size = 776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
81
The SAS System
09:07 Friday, August 29, 2014 175
The FREQ Procedure
Table of FatProt1d by Ketosis
FatProt1d
Ketosis
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
317 ‚
53 ‚
370
‚ 40.85 ‚
6.83 ‚ 47.68
‚ 85.68 ‚ 14.32 ‚
‚ 56.11 ‚ 25.12 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
248 ‚
158 ‚
406
‚ 31.96 ‚ 20.36 ‚ 52.32
‚ 61.08 ‚ 38.92 ‚
‚ 43.89 ‚ 74.88 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
565
211
776
72.81
27.19
100.00
Statistics for Table of FatProt1d by Ketosis
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
59.1349
<.0001
Likelihood Ratio Chi-Square
1
61.4319
<.0001
Continuity Adj. Chi-Square
1
57.8992
<.0001
Mantel-Haenszel Chi-Square
1
59.0587
<.0001
Phi Coefficient
0.2761
Contingency Coefficient
0.2661
Cramer's V
0.2761
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
317
Left-sided Pr <= F
1.0000
Right-sided Pr >= F
4.590E-15
Table Probability (P)
Two-sided Pr <= P
3.369E-15
7.508E-15
Sample Size = 776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
82
The SAS System
09:07 Friday, August 29, 2014 176
The FREQ Procedure
Table of CalvProb by Ketosis
CalvProb
Ketosis
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
488 ‚
173 ‚
661
‚ 62.89 ‚ 22.29 ‚ 85.18
‚ 73.83 ‚ 26.17 ‚
‚ 86.37 ‚ 81.99 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
77 ‚
38 ‚
115
‚
9.92 ‚
4.90 ‚ 14.82
‚ 66.96 ‚ 33.04 ‚
‚ 13.63 ‚ 18.01 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
565
211
776
72.81
27.19
100.00
Statistics for Table of CalvProb by Ketosis
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
2.3360
0.1264
Likelihood Ratio Chi-Square
1
2.2598
0.1328
Continuity Adj. Chi-Square
1
2.0018
0.1571
Mantel-Haenszel Chi-Square
1
2.3330
0.1267
Phi Coefficient
0.0549
Contingency Coefficient
0.0548
Cramer's V
0.0549
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
488
Left-sided Pr <= F
0.9478
Right-sided Pr >= F
0.0801
Table Probability (P)
Two-sided Pr <= P
0.0279
0.1399
Sample Size = 776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
83
The SAS System
09:07 Friday, August 29, 2014 198
The FREQ Procedure
Table of CalvProb by Metritis
CalvProb
Metritis
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
546 ‚
115 ‚
661
‚ 70.36 ‚ 14.82 ‚ 85.18
‚ 82.60 ‚ 17.40 ‚
‚ 90.10 ‚ 67.65 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
60 ‚
55 ‚
115
‚
7.73 ‚
7.09 ‚ 14.82
‚ 52.17 ‚ 47.83 ‚
‚
9.90 ‚ 32.35 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
606
170
776
78.09
21.91
100.00
Statistics for Table of CalvProb by Metritis
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
53.0142
<.0001
Likelihood Ratio Chi-Square
1
45.7800
<.0001
Continuity Adj. Chi-Square
1
51.2505
<.0001
Mantel-Haenszel Chi-Square
1
52.9459
<.0001
Phi Coefficient
0.2614
Contingency Coefficient
0.2529
Cramer's V
0.2614
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
546
Left-sided Pr <= F
1.0000
Right-sided Pr >= F
1.294E-11
Table Probability (P)
Two-sided Pr <= P
1.006E-11
1.419E-11
Sample Size = 776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
84
The SAS System
09:07 Friday, August 29, 2014 199
The FREQ Procedure
Table of Ketosis by Metritis
Ketosis
Metritis
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
451 ‚
114 ‚
565
‚ 58.12 ‚ 14.69 ‚ 72.81
‚ 79.82 ‚ 20.18 ‚
‚ 74.42 ‚ 67.06 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
155 ‚
56 ‚
211
‚ 19.97 ‚
7.22 ‚ 27.19
‚ 73.46 ‚ 26.54 ‚
‚ 25.58 ‚ 32.94 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
606
170
776
78.09
21.91
100.00
Statistics for Table of Ketosis by Metritis
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
3.6361
0.0565
Likelihood Ratio Chi-Square
1
3.5355
0.0601
Continuity Adj. Chi-Square
1
3.2737
0.0704
Mantel-Haenszel Chi-Square
1
3.6314
0.0567
Phi Coefficient
0.0685
Contingency Coefficient
0.0683
Cramer's V
0.0685
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
451
Left-sided Pr <= F
0.9763
Right-sided Pr >= F
0.0365
Table Probability (P)
Two-sided Pr <= P
0.0128
0.0638
Sample Size = 776
The SAS System
09:07 Friday, August 29, 2014 221
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.NIENKESTUDY
Indigest
2
binary logit
Fisher's scoring
Number of Observations Read
Number of Observations Used
776
776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
85
Response Profile
Ordered
Value
1
2
Indigest
Total
Frequency
1
0
133
643
Probability modeled is Indigest='1'.
Backward Elimination Procedure
Class Level Information
Design
Variables
Class
Value
Parity
Mult
Prim
0
1
BCS0C
high
low
med
1
0
0
CalvProb
0
1
0
1
Milkfever
0
1
0
1
Ketosis
0
1
0
1
FatProt1d
0
1
0
1
0
0
1
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
86
The SAS System
09:07 Friday, August 29, 2014 222
The LOGISTIC Procedure
Step
0. The following effects were entered:
Intercept
Parity
BCS0C
FatProt1d
CalvProb
Milkfever
Ketosis
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
712.950
717.604
710.950
672.919
710.152
656.919
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Step
Chi-Square
DF
Pr > ChiSq
54.0310
57.3696
48.3723
7
7
7
<.0001
<.0001
<.0001
1. Effect Milkfever is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
712.950
717.604
710.950
671.278
703.857
657.278
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
87
The SAS System
09:07 Friday, August 29, 2014 223
The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
53.6721
56.6199
47.8748
6
6
6
<.0001
<.0001
<.0001
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.3717
1
0.5421
2. Effect CalvProb is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
712.950
717.604
710.950
671.619
699.544
659.619
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
51.3304
54.5098
46.1337
5
5
5
<.0001
<.0001
<.0001
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
2.8386
2
0.2419
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
88
The SAS System
09:07 Friday, August 29, 2014 224
The LOGISTIC Procedure
Step
3. Effect BCS0C is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
712.950
717.604
710.950
672.569
691.186
664.569
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
46.3803
47.0908
43.3157
3
3
3
<.0001
<.0001
<.0001
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
8.7557
4
0.0675
NOTE: No (additional) effects met the 0.1 significance level for removal from the model.
Summary of Backward Elimination
Step
1
2
3
Effect
Removed
Milkfever
CalvProb
BCS0C
DF
Number
In
Wald
Chi-Square
Pr > ChiSq
1
1
2
5
4
3
0.3693
2.4414
4.1228
0.5434
0.1182
0.1273
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
89
The SAS System
09:07 Friday, August 29, 2014 225
The LOGISTIC Procedure
Type 3 Analysis of Effects
Effect
DF
Wald
Chi-Square
Pr > ChiSq
1
1
1
12.2792
3.3938
12.7975
0.0005
0.0654
0.0003
Parity
FatProt1d
Ketosis
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
Parity
Prim
FatProt1d 1
Ketosis
1
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
-1.7944
-0.8138
0.3897
0.7707
0.1826
0.2323
0.2116
0.2154
96.5716
12.2792
3.3938
12.7975
<.0001
0.0005
0.0654
0.0003
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
60.2
24.7
15.1
85519
Somers' D
Gamma
Tau-a
c
0.355
0.418
0.101
0.677
Odds Ratio Estimates and Profile-Likelihood Confidence Intervals
Effect
Parity
Prim vs Mult
FatProt1d 1 vs 0
Ketosis
1 vs 0
Unit
Estimate
1.0000
1.0000
1.0000
0.443
1.477
2.161
95% Confidence Limits
0.278
0.978
1.415
0.692
2.245
3.297
Odds Ratio Estimates and Wald Confidence Intervals
Effect
Parity
Prim vs Mult
FatProt1d 1 vs 0
Ketosis
1 vs 0
Unit
Estimate
1.0000
1.0000
1.0000
0.443
1.477
2.161
95% Confidence Limits
0.281
0.975
1.417
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
0.699
2.235
3.297
90
The SAS System
09:07 Friday, August 29, 2014 226
The FREQ Procedure
Table of FatProt1d by Indigest
FatProt1d
Indigest
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
323 ‚
47 ‚
370
‚ 41.62 ‚
6.06 ‚ 47.68
‚ 87.30 ‚ 12.70 ‚
‚ 50.23 ‚ 35.34 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
320 ‚
86 ‚
406
‚ 41.24 ‚ 11.08 ‚ 52.32
‚ 78.82 ‚ 21.18 ‚
‚ 49.77 ‚ 64.66 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
643
133
776
82.86
17.14
100.00
Statistics for Table of FatProt1d by Indigest
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
9.8011
0.0017
Likelihood Ratio Chi-Square
1
9.9492
0.0016
Continuity Adj. Chi-Square
1
9.2131
0.0024
Mantel-Haenszel Chi-Square
1
9.7884
0.0018
Phi Coefficient
0.1124
Contingency Coefficient
0.1117
Cramer's V
0.1124
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
323
Left-sided Pr <= F
0.9994
Right-sided Pr >= F
0.0011
Table Probability (P)
Two-sided Pr <= P
5.477E-04
0.0022
Sample Size = 776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
91
The SAS System
09:07 Friday, August 29, 2014 227
The FREQ Procedure
Table of Parity by Indigest
Parity
Indigest
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Mult
‚
354 ‚
103 ‚
457
‚ 45.62 ‚ 13.27 ‚ 58.89
‚ 77.46 ‚ 22.54 ‚
‚ 55.05 ‚ 77.44 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Prim
‚
289 ‚
30 ‚
319
‚ 37.24 ‚
3.87 ‚ 41.11
‚ 90.60 ‚
9.40 ‚
‚ 44.95 ‚ 22.56 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
643
133
776
82.86
17.14
100.00
Statistics for Table of Parity by Indigest
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
22.8188
<.0001
Likelihood Ratio Chi-Square
1
24.2800
<.0001
Continuity Adj. Chi-Square
1
21.9034
<.0001
Mantel-Haenszel Chi-Square
1
22.7894
<.0001
Phi Coefficient
-0.1715
Contingency Coefficient
0.1690
Cramer's V
-0.1715
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
354
Left-sided Pr <= F
7.296E-07
Right-sided Pr >= F
1.0000
Table Probability (P)
Two-sided Pr <= P
4.789E-07
1.078E-06
Sample Size = 776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
92
The SAS System
09:07 Friday, August 29, 2014 228
The FREQ Procedure
Table of Ketosis by Indigest
Ketosis
Indigest
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
495 ‚
70 ‚
565
‚ 63.79 ‚
9.02 ‚ 72.81
‚ 87.61 ‚ 12.39 ‚
‚ 76.98 ‚ 52.63 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
148 ‚
63 ‚
211
‚ 19.07 ‚
8.12 ‚ 27.19
‚ 70.14 ‚ 29.86 ‚
‚ 23.02 ‚ 47.37 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
643
133
776
82.86
17.14
100.00
Statistics for Table of Ketosis by Indigest
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
33.0094
<.0001
Likelihood Ratio Chi-Square
1
30.3638
<.0001
Continuity Adj. Chi-Square
1
31.7909
<.0001
Mantel-Haenszel Chi-Square
1
32.9669
<.0001
Phi Coefficient
0.2062
Contingency Coefficient
0.2020
Cramer's V
0.2062
Fisher's Exact Test
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
495
Left-sided Pr <= F
1.0000
Right-sided Pr >= F
3.014E-08
Table Probability (P)
Two-sided Pr <= P
2.047E-08
4.920E-08
Sample Size = 776
The SAS System
09:07 Friday, August 29, 2014 177
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.NIENKESTUDY
Mastitis
2
binary logit
Fisher's scoring
Number of Observations Read
Number of Observations Used
776
776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
93
Response Profile
Ordered
Value
1
2
Mastitis
Total
Frequency
1
0
109
667
Probability modeled is Mastitis='1'.
Backward Elimination Procedure
Class Level Information
Design
Variables
Class
Value
Parity
Mult
Prim
0
1
BCS0C
high
low
med
1
0
0
CalvProb
0
1
0
1
Milkfever
0
1
0
1
Ketosis
0
1
0
1
FatProt1d
0
1
0
1
0
0
1
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
94
The SAS System
09:07 Friday, August 29, 2014 178
The LOGISTIC Procedure
Step
0. The following effects were entered:
Intercept
Parity
BCS0C
FatProt1d
CalvProb
Milkfever
Ketosis
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
631.809
636.463
629.809
633.136
670.370
617.136
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Step
Chi-Square
DF
Pr > ChiSq
12.6725
12.3063
11.9997
7
7
7
0.0805
0.0909
0.1006
1. Effect Milkfever is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
631.809
636.463
629.809
631.167
663.746
617.167
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
95
The SAS System
09:07 Friday, August 29, 2014 179
The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
12.6418
12.2574
11.9562
6
6
6
0.0491
0.0565
0.0630
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
0.0314
1
0.8594
2. Effect CalvProb is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
631.809
636.463
629.809
629.452
657.377
617.452
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
12.3573
11.9714
11.6796
5
5
5
0.0302
0.0352
0.0395
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
0.3079
2
0.8573
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
96
The SAS System
09:07 Friday, August 29, 2014 180
The LOGISTIC Procedure
Step
3. Effect Ketosis is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
631.809
636.463
629.809
628.660
651.931
618.660
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
11.1485
10.7769
10.5302
4
4
4
0.0249
0.0292
0.0324
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
1.4910
3
0.6843
4. Effect Parity is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
The SAS System
09:07 Friday, August 29, 2014 181
The LOGISTIC Procedure
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
631.809
636.463
629.809
628.367
646.984
620.367
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Chi-Square
DF
Pr > ChiSq
9.4417
3
0.0240
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
97
Score
Wald
9.1229
8.9187
3
3
0.0277
0.0304
Residual Chi-Square Test
Step
Chi-Square
DF
Pr > ChiSq
3.1914
4
0.5263
5. Effect FatProt1d is removed:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
631.809
636.463
629.809
628.693
642.656
622.693
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
98
The SAS System
09:07 Friday, August 29, 2014 182
The LOGISTIC Procedure
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
7.1158
6.8394
6.6820
2
2
2
0.0285
0.0327
0.0354
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
5.4811
5
0.3600
NOTE: No (additional) effects met the 0.1 significance level for removal from the model.
Summary of Backward Elimination
Step
1
2
3
4
5
Effect
Removed
Milkfever
CalvProb
Ketosis
Parity
FatProt1d
DF
Number
In
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
5
4
3
2
1
0.0314
0.2767
1.1837
1.6759
2.2991
0.8594
0.5989
0.2766
0.1955
0.1295
Type 3 Analysis of Effects
Effect
DF
Wald
Chi-Square
Pr > ChiSq
BCS0C
2
6.6820
0.0354
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
BCS0C
high
BCS0C
med
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
-1.0986
-0.5328
-1.1270
1.1547
1.1611
1.1723
0.9052
0.2106
0.9242
0.3414
0.6463
0.3364
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
99
The SAS System
09:07 Friday, August 29, 2014 183
The LOGISTIC Procedure
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
28.8
15.8
55.4
72703
Somers' D
Gamma
Tau-a
c
0.129
0.290
0.031
0.565
Odds Ratio Estimates and Profile-Likelihood Confidence Intervals
Effect
BCS0C high vs low
BCS0C med vs low
Unit
Estimate
1.0000
1.0000
0.587
0.324
95% Confidence Limits
0.074
0.040
11.942
6.672
Odds Ratio Estimates and Wald Confidence Intervals
Effect
BCS0C high vs low
BCS0C med vs low
Unit
Estimate
1.0000
1.0000
0.587
0.324
95% Confidence Limits
The SAS System
0.060
0.033
5.714
3.224
09:07 Friday, August 29, 2014 184
The FREQ Procedure
Table of FatProt1d by Mastitis
FatProt1d
Mastitis
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0
‚
326 ‚
44 ‚
370
‚ 42.01 ‚
5.67 ‚ 47.68
‚ 88.11 ‚ 11.89 ‚
‚ 48.88 ‚ 40.37 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1
‚
341 ‚
65 ‚
406
‚ 43.94 ‚
8.38 ‚ 52.32
‚ 83.99 ‚ 16.01 ‚
‚ 51.12 ‚ 59.63 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
667
109
776
85.95
14.05
100.00
Statistics for Table of FatProt1d by Mastitis
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
1
2.7190
0.0992
Likelihood Ratio Chi-Square
1
2.7379
0.0980
Continuity Adj. Chi-Square
1
2.3886
0.1222
Mantel-Haenszel Chi-Square
1
2.7154
0.0994
Phi Coefficient
0.0592
Contingency Coefficient
0.0591
Cramer's V
0.0592
Fisher's Exact Test
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
100
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Cell (1,1) Frequency (F)
326
Left-sided Pr <= F
0.9605
Right-sided Pr >= F
0.0608
Table Probability (P)
Two-sided Pr <= P
0.0213
0.1205
Sample Size = 776
The SAS System
09:07 Friday, August 29, 2014 185
The FREQ Procedure
Table of BCS0C by Mastitis
BCS0C
Mastitis
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚0
‚1
‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
high
‚
414 ‚
81 ‚
495
‚ 53.35 ‚ 10.44 ‚ 63.79
‚ 83.64 ‚ 16.36 ‚
‚ 62.07 ‚ 74.31 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
low
‚
3 ‚
1 ‚
4
‚
0.39 ‚
0.13 ‚
0.52
‚ 75.00 ‚ 25.00 ‚
‚
0.45 ‚
0.92 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
med
‚
250 ‚
27 ‚
277
‚ 32.22 ‚
3.48 ‚ 35.70
‚ 90.25 ‚
9.75 ‚
‚ 37.48 ‚ 24.77 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
667
109
776
85.95
14.05
100.00
Statistics for Table of BCS0C by Mastitis
Statistic
DF
Value
Prob
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Chi-Square
2
6.8394
0.0327
Likelihood Ratio Chi-Square
2
7.1158
0.0285
Mantel-Haenszel Chi-Square
1
6.3612
0.0117
Phi Coefficient
0.0939
Contingency Coefficient
0.0935
Cramer's V
0.0939
WARNING: 33% of the cells have expected counts less
than 5. Chi-Square may not be a valid test.
Sample Size = 776
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
101
Appendix D: Kaplan-Meier survival curve time to pregnancy and FPR1day
Kaplan-Meier survival curve
Survival time
DOPN
Endpoint
Preg300DIM
Factor codes
FatProt1d
Cases summary
Number of events
Number censored
Factor
N
%
N
%
0
363
83.64
71
16.36
1
391
82.32
84
17.68
Overall
754
82.95
155
17.05
Mean and median survival
Factor
Mean
SE
95% CI for the mean
Median
0
147.622
3.802
140.170 to 155.075
120.000
1
150.363
3.738
143.037 to 157.689
121.000
Overall
149.049
1.689
145.738 to 152.359
121.000
Survival table [Hide]
Factor
0
1
Survival
Survival
Standard
Survival
Standard
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
Total sample size
434
475
909
95% CI for the median
118.000 to 123.000
118.000 to 126.000
119.000 to 123.000
Overall
Survival
Standard
102
time
57
69
74
75
76
77
78
79
80
81
82
83
84
85
86
87
89
90
93
94
95
96
97
98
99
100
101
102
103
104
105
106
108
109
110
112
113
115
116
117
118
119
120
121
122
123
124
125
Proportion
0.998
0.975
0.910
0.873
0.836
0.797
0.758
0.703
0.691
0.682
0.666
0.661
0.647
0.633
0.620
0.617
0.613
0.610
0.608
0.606
0.603
0.596
0.594
0.587
0.583
0.569
0.555
0.534
0.513
0.499
0.483
0.469
0.452
0.448
0.443
Error
0.00230
0.00754
0.0137
0.0160
0.0178
0.0193
0.0206
0.0219
0.0222
0.0224
0.0226
0.0227
0.0229
0.0231
0.0233
0.0233
0.0234
0.0234
0.0234
0.0235
0.0235
0.0236
0.0236
0.0236
0.0237
0.0238
0.0239
0.0240
0.0240
0.0240
0.0240
0.0240
0.0239
0.0239
0.0239
Proportion
0.973
0.937
0.903
0.867
0.821
0.772
0.711
0.705
0.690
0.673
0.658
0.643
0.629
0.620
0.618
0.616
0.614
0.612
0.610
0.608
0.601
0.599
0.593
0.588
0.586
0.584
0.582
0.580
0.569
0.558
0.541
0.515
0.502
0.485
0.472
0.465
0.456
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
Error
0.00750
0.0112
0.0136
0.0156
0.0176
0.0193
0.0208
0.0210
0.0212
0.0215
0.0218
0.0220
0.0222
0.0223
0.0223
0.0223
0.0224
0.0224
0.0224
0.0224
0.0225
0.0225
0.0226
0.0226
0.0226
0.0226
0.0227
0.0227
0.0228
0.0228
0.0229
0.0230
0.0231
0.0231
0.0230
0.0230
0.0230
Proportion
0.999
0.974
0.924
0.889
0.852
0.809
0.765
0.707
0.698
0.686
0.670
0.660
0.645
0.631
0.620
0.619
0.618
0.617
0.613
0.612
0.610
0.608
0.607
0.603
0.602
0.601
0.594
0.591
0.587
0.583
0.582
0.581
0.569
0.557
0.538
0.514
0.500
0.484
0.470
0.459
0.457
0.450
103
Error
0.00110
0.00532
0.00879
0.0104
0.0118
0.0130
0.0141
0.0151
0.0152
0.0154
0.0156
0.0157
0.0159
0.0160
0.0161
0.0161
0.0161
0.0161
0.0162
0.0162
0.0162
0.0162
0.0162
0.0162
0.0162
0.0163
0.0163
0.0163
0.0163
0.0164
0.0164
0.0164
0.0165
0.0165
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
126
127
128
129
131
134
135
137
139
140
141
142
143
144
145
146
147
148
149
151
152
153
154
155
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
173
174
175
176
177
178
179
180
181
182
0.434
0.422
0.420
0.408
0.406
0.401
0.399
0.396
0.387
0.382
0.378
0.373
0.371
0.366
0.363
0.361
0.354
0.352
0.345
0.335
0.326
0.321
0.314
0.311
0.306
0.304
0.297
0.292
0.285
0.283
0.280
0.278
0.275
0.273
0.270
0.268
0.265
0.0239
0.0238
0.0238
0.0237
0.0236
0.0236
0.0236
0.0236
0.0235
0.0234
0.0234
0.0233
0.0233
0.0232
0.0232
0.0231
0.0231
0.0230
0.0229
0.0228
0.0226
0.0225
0.0224
0.0224
0.0223
0.0222
0.0221
0.0220
0.0218
0.0218
0.0217
0.0217
0.0216
0.0216
0.0215
0.0215
0.0214
0.448
0.439
0.430
0.419
0.417
0.411
0.406
0.404
0.400
0.397
0.395
0.393
0.391
0.386
0.384
0.382
0.369
0.357
0.346
0.337
0.330
0.324
0.319
0.310
0.306
0.303
0.299
0.297
0.294
0.290
0.288
0.285
0.283
0.281
0.279
-
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
0.0230
0.0229
0.0229
0.0228
0.0228
0.0228
0.0227
0.0227
0.0227
0.0227
0.0226
0.0226
0.0226
0.0226
0.0225
0.0225
0.0224
0.0223
0.0221
0.0220
0.0219
0.0218
0.0217
0.0216
0.0215
0.0214
0.0214
0.0213
0.0213
0.0212
0.0211
0.0211
0.0211
0.0210
0.0210
-
0.441
0.431
0.425
0.414
0.413
0.409
0.406
0.404
0.403
0.400
0.394
0.390
0.389
0.387
0.384
0.383
0.382
0.380
0.379
0.376
0.374
0.372
0.368
0.360
0.351
0.341
0.332
0.326
0.319
0.315
0.308
0.305
0.300
0.296
0.291
0.289
0.285
0.284
0.283
0.280
0.279
0.278
0.277
0.276
0.273
0.272
104
0.0166
0.0165
0.0165
0.0164
0.0164
0.0164
0.0164
0.0164
0.0164
0.0164
0.0163
0.0163
0.0163
0.0163
0.0162
0.0162
0.0162
0.0162
0.0162
0.0162
0.0162
0.0161
0.0161
0.0161
0.0160
0.0159
0.0158
0.0157
0.0156
0.0156
0.0155
0.0155
0.0154
0.0153
0.0153
0.0152
0.0152
0.0152
0.0151
0.0151
0.0151
0.0151
0.0151
0.0150
0.0150
0.0150
183
185
186
187
190
191
192
193
194
195
196
197
199
200
201
202
203
204
205
206
207
208
209
210
212
214
216
217
221
222
226
227
228
230
233
234
236
237
240
241
242
243
244
245
246
247
248
251
253
0.263
0.256
0.251
0.248
0.243
0.238
0.230
0.228
0.223
0.217
0.215
0.212
0.206
0.204
0.201
0.195
0.192
0.189
0.186
0.182
0.179
0.176
0.172
0.166
0.162
0.0213
0.0212
0.0210
0.0210
0.0209
0.0207
0.0205
0.0205
0.0203
0.0202
0.0201
0.0200
0.0199
0.0198
0.0197
0.0196
0.0195
0.0195
0.0194
0.0193
0.0192
0.0192
0.0191
0.0189
0.0189
0.276
0.274
0.272
0.269
0.267
0.265
0.262
0.260
0.258
0.255
0.253
0.251
0.246
0.244
0.237
0.230
0.222
0.220
0.215
0.212
0.210
0.207
0.205
0.202
0.200
0.197
0.195
0.192
0.189
0.187
0.184
-
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
0.0209
0.0209
0.0208
0.0208
0.0207
0.0207
0.0206
0.0206
0.0205
0.0205
0.0204
0.0203
0.0202
0.0202
0.0200
0.0198
0.0196
0.0196
0.0194
0.0194
0.0193
0.0192
0.0192
0.0191
0.0190
0.0190
0.0189
0.0188
0.0187
0.0187
0.0186
-
0.270
0.266
0.265
0.262
0.259
0.258
0.254
0.252
0.251
0.249
0.248
0.243
0.242
0.240
0.235
0.231
0.226
0.221
0.217
0.216
0.211
0.209
0.208
0.207
0.204
0.201
0.200
0.198
0.197
0.196
0.194
0.193
0.191
0.190
0.189
0.186
0.183
0.181
0.180
0.175
0.174
105
0.0149
0.0149
0.0149
0.0148
0.0148
0.0148
0.0147
0.0147
0.0146
0.0146
0.0146
0.0145
0.0145
0.0144
0.0144
0.0143
0.0142
0.0141
0.0140
0.0140
0.0139
0.0139
0.0139
0.0138
0.0138
0.0137
0.0137
0.0137
0.0137
0.0136
0.0136
0.0136
0.0136
0.0135
0.0135
0.0134
0.0134
0.0134
0.0133
0.0133
0.0132
255
0.159
0.0188
256
257
0.155
0.0187
258
260
261
270
271
272
273
0.151
0.0186
274
275
279
280
281
0.147
0.0186
282
283
284
285
0.143
0.0185
286
0.138
0.0184
287
0.134
0.0184
288
289
0.125
0.0182
292
295
297
0.121
0.0181
300
Endpoint: Observed n
363.0
Expected n
354.4
Observed/Expected
1.0242
Comparison of survival curves (Logrank test)
Chi-squared
0.4044
DF
1
Significance
P = 0.5248
Hazard ratiosa with 95% Confidence Interval
Factor
0
1
0.9554
0
0.8281 to 1.1022
1.0467
1
0.9072 to 1.2077
0.179
0.176
0.173
0.170
0.168
0.165
0.162
0.159
0.153
0.150
0.147
0.144
0.140
0.137
-
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
0.0184
0.0183
0.0183
0.0182
0.0181
0.0180
0.0179
0.0178
0.0177
0.0176
0.0175
0.0174
0.0173
0.0172
391.0
399.6
0.9785
0.169
0.168
0.166
0.165
0.163
0.161
0.158
0.156
0.155
0.153
0.151
0.146
0.144
0.142
0.137
0.135
0.133
0.130
-
106
0.0131
0.0131
0.0131
0.0131
0.0130
0.0130
0.0129
0.0129
0.0129
0.0128
0.0128
0.0127
0.0127
0.0127
0.0126
0.0125
0.0125
0.0124
-
N.S. van de Burgwal, BSc, 3382036, established at the University of Florida
107