V.A.Morgan document1
15 March 2016
USING WEIGHTS WITH SHIP DATA
Version: 05 July 2012
Contents
USING WEIGHTS WITH SHIP DATA .................................................................... 1
General introduction ................................................................................ 2
Weights in Stata...................................................................................... 3
Weights in SPSS ...................................................................................... 4
Weights in SAS ........................................................................................ 5
APPENDICES ............................................................................................. 6
Stata example output ............................................................................... 6
Stata: Mean ........................................................................................ 6
Stata: Tabulate .................................................................................... 6
Stata: Logistic regression ........................................................................ 7
SPSS example output ................................................................................ 9
SPSS: Complex Samples Analysis Plan .......................................................... 9
SPSS: Complex Samples Descriptives .......................................................... 10
SPSS: Complex Samples Tables ................................................................. 11
SPSS: Complex Samples Logistic regression .................................................. 15
SAS example output ................................................................................ 17
SAS: The SURVEYMEANS Procedure ............................................................ 17
SAS: The SURVEYFREQ Procedure .............................................................. 18
SAS: The SURVEYLOGISTIC Procedure ......................................................... 19
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General introduction
1. Weighted analyses should be only be done using appropriate statistical software:
 Stata svy or pweight commands
 SPSS Complex Samples
 SAS Survey Procedures
2. The weights to use in Stata, SPSS Complex Samples and SAS Survey Procedures are in
the variable:
 weightsa
3. Weightsa may also be used for subgroup analyses using Stata commands, SPSS Complex
Samples and SAS Survey Procedures.
4. If working with the full SHIP dataset based on 1825 survey participants, using weights
results in a population N of 7955. For SHIP, ‘population’ refers to the individuals
ascertained in the screening phase This N will be smaller for subsamples (e.g. those
meeting full ICD-10 criteria for psychosis; schizophrenia only etc.).
5. It is very important that weightsa be used only as a sampling (or probably) weight.
Using it as a weight in a Stata [fweight=…] option, the SPSS “Weight by…” command or as
a WEIGHT or FREQ variable in a normal SAS proc will lead the program to believe that 7955
participants were actually interviewed leading to extremely serious errors in many
statistics.
6. When relevant strata information is included in a model, the ‘need’ for weighting is
dramatically reduced. Site strata information is not available, but our experience to date
is that when age is included in models (e.g., regression), the difference in estimates and
standard errors between weighted and unweighted data is relatively small. Nevertheless,
if it possible to use weighting, it should be done for accuracy and for uniformity across
analyses.
7. For information only: there is a variable that Andrew has created called weightsa_nrm
which gives an approximation for those using basic SPSS and basic SAS. However we are
not recommending that people work in basic SPSS or basic SAS with this variable and will
not be providing it unless the person requesting to use this variable can justify their
request.
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Weights in Stata
In Stata, there are two approaches, depending on the procedure.
(a) For many procedures (e.g. mean; logistic), you can use the [pweight=weightsa] option
(b) Other procedures (e.g. tabulate) need to be run in conjunction with the survey (svy)
feature. The survey feature can also be used with procedures that permit the
[pweight=weightsa] option.
The survey structure must be first specified in svyset if you need to use the survey
procedure:
* Specifying the survey structure before using the survey (svy) feature.
svyset [pweight=weightsa]
This will produce the following information output
pweight: weightsa
VCE: linearized
Single unit: missing
Strata 1: <one>
SU 1: <observations>
FPC 1: <zero>
* Means using (a) pweight and (b) the survey feature. Note that (b) assumes you
have specified the survey structure as above.
(a) mean age_calc [pweight = weightsa]
(b) svy: mean age_calc
* Tables using the survey feature. Note that this assumes you have specified the
survey structure as above.
svy: tabulate sex DIAGicd10, count ci
* Logistic Regression a) pweight and (b) the survey feature. Note that (b) assumes
you have specified the survey structure as above..
(a) logit anyIP_rev age_calc [pweight = weightsa]
(b) svy: logit anyIP_rev age_calc
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Weights in SPSS
The appropriate SPSS Complex Samples analysis plan is in a file called
SHIP_SPSSweighting.csaplan which is created by running the syntax below:
CSPLAN ANALYSIS
/PLAN FILE='D:\SHIPdata_files\SHIP_SPSSweighting.csaplan'
/PLANVARS ANALYSISWEIGHT=weightsa
/SRSESTIMATOR TYPE=WR
/PRINT PLAN
/DESIGN
/ESTIMATOR TYPE=WR.
You must use the SPSS Complex Samples battery of statistics with this plan. For example:
* Complex Samples Descriptives.
CSDESCRIPTIVES
/PLAN FILE='D:\SHIPdata_files\SHIP_SPSSweighting.csaplan'
/SUMMARY VARIABLES=age_calc
/MEAN
/STATISTICS SE COUNT POPSIZE CIN(95)
/MISSING SCOPE=ANALYSIS CLASSMISSING=EXCLUDE.
* Complex Samples Tabulate.
CSTABULATE
/PLAN FILE='D:\SHIPdata_files\SHIP_SPSSweighting.csaplan'
/TABLES VARIABLES=sex BY DIAGicd10
/CELLS POPSIZE COLPCT
/STATISTICS CIN(95)
/MISSING SCOPE=TABLE CLASSMISSING=EXCLUDE.
* Complex Samples Logistic Regression.
CSLOGISTIC anyIP WITH age_calc
/PLAN FILE='D:\SHIPdata_files\SHIP_SPSSweighting.csaplan'
/MODEL age_calc
/INTERCEPT INCLUDE=YES SHOW=YES
/STATISTICS PARAMETER EXP CINTERVAL
/TEST TYPE=F PADJUST=LSD
/ODDSRATIOS COVARIATE=[age_calc(1)]
/CRITERIA MXITER=100 MXSTEP=5 PCONVERGE=[1E-006 RELATIVE] LCONVERGE=[0]
CHKSEP=20 CILEVEL=95
/PRINT SUMMARY CLASSTABLE VARIABLEINFO SAMPLEINFO.
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Weights in SAS
For SAS Complex Survey Design, SAS uses specific commands when using weight variable.
For example: for Means, SAS uses Proc Surveymeans as the main command instead of
'Proc Means'. Similarly for frequency and logistic procedures, SAS uses Proc Surveyfreq
and Proc Surveylogistic respectively
SAS SURVEY PROCEDURES COMMANDS. For example:
*Complex survey design: MEAN PROCEDURE.
Proc Surveymeans data=xxx.xxxxxx;
weight weightsa;
var age_calc;
Title 'MEAN PROCEDURE FOR AGE';
run;
*Complex survey design: CROSS TABULATION.
Proc Surveyfreq data=xxx.xxxxxx ;
weight weightsa;
tables sex*DIAGICD10;
Title 'Cross tabulation of Sex by Psychotic illness';
run;
*Complex survey design: LOGISTIC REGRESSION.
Proc Surveylogistic data= xxx.xxxxxx order=internal;
weight weightsa;
Model anyIP (ref=last order=internal)= age_calc /link=glogit;
Title 'Effect of age on inpatient admission';
run;
w
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APPENDICES
Stata example output
Stata: Mean
Stata: Tabulate
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Stata: Logistic regression
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SPSS example output
SPSS: Complex Samples Analysis Plan
* SHIP_SPSSweighting_egs.sps.
* Analysis Preparation Wizard.
CSPLAN ANALYSIS
/PLAN FILE='D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan'
/PLANVARS ANALYSISWEIGHT=weightsa
/SRSESTIMATOR TYPE=WR
/PRINT PLAN
/DESIGN
/ESTIMATOR TYPE=WR.
Complex Samples: Plan
Summary
Analysis Information
Estimator Assumption
Stage 1
Sampling with
replacement
Plan File:
D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan
Weight Variable: weightsa Sampling weight by site and age stratum
SRS Estimator: Sampling with replacement
CSPLAN VIEW
/PLAN FILE='D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan' .
Complex Samples: Plan
Analysis Plan
Summary
Analysis Information
Estimator Assumption
Stage 1
Sampling with
replacement
Plan File:
D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan
Weight Variable: weightsa Sampling weight by site and age stratum
SRS Estimator: Sampling with replacement
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SPSS: Complex Samples Descriptives
* Complex Samples Descriptives.
CSDESCRIPTIVES
/PLAN FILE='D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan'
/SUMMARY VARIABLES=age_calc
/MEAN
/STATISTICS SE COUNT POPSIZE CIN(95)
/MISSING SCOPE=ANALYSIS CLASSMISSING=EXCLUDE.
Complex Samples: Descriptives
Univariate Statistics
Estimate
Standard Error
Mean
age_calc Age (calculated) in years at
time of interview
39.47
95% Confidence Interval
Lower
Upper
.273
38.93
40.00
Univariate Statistics
Population Size
Mean
age_calc Age (calculated) in years at time of
interview
7955.000
Unweighted Count
1825
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SPSS: Complex Samples Tables
* Complex Samples Tables.
CSTABULATE
/PLAN FILE='D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan'
/TABLES VARIABLES=sex BY DIAGicd10
/CELLS POPSIZE COLPCT
/STATISTICS CIN(95)
/MISSING SCOPE=TABLE CLASSMISSING=EXCLUDE.
Complex Samples: Tables
sex sex * DIAGicd10 DIP ICD-10
sex sex
Estimate
Population Size
95% Confidence Interval
0 male
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
95% Confidence Interval
1 female
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
95% Confidence Interval
Total
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
DIAGicd10 DIP
ICD-10
1 schizophrenia
2650.360
2464.343
2836.378
70.4%
67.1%
73.6%
1112.866
974.388
1251.344
29.6%
26.4%
32.9%
3763.226
3559.907
3966.545
100.0%
100.0%
100.0%
sex sex * DIAGicd10 DIP ICD-10
sex sex
Estimate
Population Size
0 male
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
1 female
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
Total
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
DIAGicd10 DIP
ICD-10
2 schizoaffective
623.920
524.582
723.257
53.6%
47.5%
59.5%
540.863
447.709
634.017
46.4%
40.5%
52.5%
1164.782
1033.930
1295.635
100.0%
100.0%
100.0%
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sex sex * DIAGicd10 DIP ICD-10
sex sex
Estimate
Population Size
95% Confidence Interval
0 male
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
95% Confidence Interval
1 female
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
95% Confidence Interval
Total
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
DIAGicd10 DIP
ICD-10
3 bipolar, mania
621.156
516.721
725.591
44.5%
38.8%
50.3%
774.091
658.974
889.208
55.5%
49.7%
61.2%
1395.247
1246.483
1544.011
100.0%
100.0%
100.0%
sex sex * DIAGicd10 DIP ICD-10
sex sex
Estimate
Population Size
0 male
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
1 female
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
Total
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
DIAGicd10 DIP
ICD-10
4 depressive
psychosis
146.112
93.097
199.128
39.7%
29.1%
51.3%
221.883
156.582
287.185
60.3%
48.7%
70.9%
367.996
284.700
451.292
100.0%
100.0%
100.0%
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sex sex * DIAGicd10 DIP ICD-10
sex sex
Estimate
Population Size
95% Confidence Interval
0 male
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
95% Confidence Interval
1 female
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
95% Confidence Interval
Total
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
DIAGicd10 DIP
ICD-10
5 delusional
disorders and other
non-organic
psychosis
303.380
225.754
381.007
72.0%
61.4%
80.7%
117.849
70.240
165.459
28.0%
19.3%
38.6%
421.230
330.999
511.461
100.0%
100.0%
100.0%
sex sex * DIAGicd10 DIP ICD-10
sex sex
Estimate
Population Size
0 male
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
1 female
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
Total
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
DIAGicd10 DIP
ICD-10
6 severe
depression without
psychosis
288.228
212.997
363.459
40.2%
32.4%
48.6%
427.942
338.840
517.044
59.8%
51.4%
67.6%
716.170
601.809
830.532
100.0%
100.0%
100.0%
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sex sex * DIAGicd10 DIP ICD-10
sex sex
Estimate
Population Size
95% Confidence Interval
0 male
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
95% Confidence Interval
1 female
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
95% Confidence Interval
Total
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
DIAGicd10 DIP
ICD-10
7 screen-positive
for psychosis but
did not meet full
criteria for ICD-10
psychosis
79.350
40.256
118.444
62.8%
41.5%
80.1%
46.999
13.491
80.507
37.2%
19.9%
58.5%
126.349
75.013
177.685
100.0%
100.0%
100.0%
sex sex * DIAGicd10 DIP ICD-10
sex sex
Estimate
Population Size
0 male
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
1 female
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
Estimate
Population Size
Total
95% Confidence Interval
Lower
Upper
Estimate
% within DIAGicd10 DIP ICD-10
95% Confidence Interval
Lower
Upper
DIAGicd10
DIP ICD-10
Total
4712.507
4508.928
4916.086
59.2%
56.8%
61.6%
3242.493
3043.639
3441.347
40.8%
38.4%
43.2%
7955.000
7826.394
8083.606
100.0%
100.0%
100.0%
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SPSS: Complex Samples Logistic regression
* Complex Samples Logistic Regression.
CSLOGISTIC anyIP WITH age_calc
/PLAN FILE='D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan'
/MODEL age_calc
/INTERCEPT INCLUDE=YES SHOW=YES
/STATISTICS PARAMETER EXP CINTERVAL
/TEST TYPE=F PADJUST=LSD
/ODDSRATIOS COVARIATE=[age_calc(1)]
/CRITERIA MXITER=100 MXSTEP=5 PCONVERGE=[1E-006 RELATIVE] LCONVERGE=[0] CHKSEP=20
CILEVEL=95
/PRINT SUMMARY CLASSTABLE VARIABLEINFO SAMPLEINFO.
Complex Samples: Logistic Regression
Sample Design Information
N
Unweighted Cases
Valid
Invalid
Total
1825
0
1825
7955.000
1
1825
1824
Population Size
Strata
Units
Sampling Design Degrees of Freedom
Stage 1
Categorical Variable Information
Weighted Count Weighted Percent
0
no/na/dk
4552.607
57.2%
anyIP Any inpatient admissions - past
yeara
1 yesb
3402.393
42.8%
Population Size
7955.000
100.0%
a. Dependent Variable
b. Reference Category
Covariate Information
Mean
age_calc Age (calculated) in years at
time of interview
39.47
Pseudo R Squares
Cox and Snell
.002
Nagelkerke
.003
McFadden
.002
Dependent Variable: anyIP
Any inpatient admissions past year (reference category
= 1 yes)
Model: (Intercept), age_calc
Source
(Corrected Model)
(Intercept)
age_calc
Tests of Model Effects
df1
df2
1.000
1824.000
1.000
1824.000
1.000
1824.000
Wald F
3.883
.101
3.883
Sig.
.049
.751
.049
Dependent Variable: anyIP Any inpatient admissions - past year
(reference category = 1 yes)
Model: (Intercept), age_calc
Parameter Estimates
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anyIP Any inpatient admissions - past
year
0 no/na/dk
Parameter
B
(Intercept)
age_calc
-.057
.009
anyIP Any inpatient admissions - past year
Parameter Estimates
Parameter
(Intercept)
age_calc
0 no/na/dk
95% Confidence Interval
Lower
Upper
-.407
.293
4.109E-005
.018
Exp(B)
.945
1.009
95% Confidence Interval for Exp(B)
Lower
Upper
.666
1.341
1.000
1.018
Dependent Variable: anyIP Any inpatient admissions - past year (reference category = 1 yes)
Model: (Intercept), age_calc
Observed
0 no/na/dk
1 yes
Overall Percent
Classification
Predicted
0 no/na/dk
1 yes
Percent Correct
4552.607
.000
100.0%
3402.393
.000
0.0%
100.0%
0.0%
57.2%
Dependent Variable: anyIP Any inpatient admissions - past year
(reference category = 1 yes)
Model: (Intercept), age_calc
Odds Ratiosa
anyIP Any inpatient admissions - past
year
Units of Change
age_calc Age (calculated) in years at
time of interview
1.000
Units of Change
age_calc Age (calculated) in years at time of
1.000
interview
Units of Change
age_calc Age (calculated) in years at time of
1.000
interview
Odds Ratio
0 no/na/dk
Odds Ratiosa
anyIP Any inpatient admissions - past year
0 no/na/dk
Odds Ratiosa
anyIP Any inpatient admissions - past year
0 no/na/dk
1.009
95% Confidence
Interval
Lower
1.000
95% Confidence
Interval
Upper
1.018
Dependent Variable: anyIP Any inpatient admissions - past year (reference category = 1 yes)
Model: (Intercept), age_calca
a. Factors and covariates used in the computation are fixed at the following values: age_calc Age (calculated) in years at
time of interview=39.47
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SAS example output
SAS: The SURVEYMEANS Procedure
MEAN PROCEDURE FOR AGE
Data Summary
Number of Observations
1825
Sum of Weights
7955
Statistics
Variable
Label
AGE_CALC
Age (calculated) in years at time of
interview
N
Mean
182 39.46561
5
7
Std Error
of Mean
95% CL for Mean
0.272877 38.930432 40.000801
2
2
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SAS: The SURVEYFREQ Procedure
Cross tabulation of Sex by Psychotic illness
Data Summary
Number of Observations
1825
Sum of Weights
7955
Table of SEX by DIAGICD10
Weighted
Frequency Frequency
Std Dev of
Wgt Freq Percent
Std Err of
Percent
94.84571 33.3169
1.1668
623.91958
50.64980
7.8431
0.6389
141
621.15590
53.24893
7.8084
0.6662
depressive psychosis
33
146.11248
27.03131
1.8367
0.3392
delusional disorders and other nonorganic psychosis
65
303.38049
39.57960
3.8137
0.4950
severe depression without psychosis
62
288.22831
38.35848
3.6232
0.4800
screen-positive for psychosis but did not
meet full criteria for ICD-10 psychosis
17
79.34982
19.93295
0.9975
0.2504
Total
1087
4713
103.79991 59.2396
1.2216
female schizophrenia
245
1113
70.60636 13.9895
0.8748
schizoaffective
136
540.86291
47.49690
6.7990
0.5987
bipolar, mania
178
774.09087
58.69530
9.7309
0.7339
depressive psychosis
48
221.88316
33.29560
2.7892
0.4174
delusional disorders and other nonorganic psychosis
27
117.84948
24.27508
1.4815
0.3048
severe depression without psychosis
96
427.94215
45.43072
5.3795
0.5684
8
46.99893
17.08476
0.5908
0.2144
Total
738
3242
101.39056 40.7604
1.2216
schizophrenia
857
3763
103.66711 47.3064
1.2396
schizoaffective
293
1165
66.71838 14.6421
0.8442
bipolar, mania
319
1395
75.85090 17.5392
0.9430
depressive psychosis
81
367.99565
42.47047
4.6260
0.5315
delusional disorders and other nonorganic psychosis
92
421.22997
46.00675
5.2952
0.5745
severe depression without psychosis
158
716.17047
58.31017
9.0028
0.7259
25
126.34875
26.17484
1.5883
0.3282
1825
7955
SEX
DIAGICD10
male
schizophrenia
612
2650
schizoaffective
157
bipolar, mania
screen-positive for psychosis but did not
meet full criteria for ICD-10 psychosis
Total
screen-positive for psychosis but did not
meet full criteria for ICD-10 psychosis
Total
65.57273 100.000
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SAS: The SURVEYLOGISTIC Procedure
Logistic regression
Effect of age on inpatient admission
Model Information
Data Set
SAHA.SHIPDATA
Response Variable
ANYIP
Number of Response Levels
2
Weight Variable
weightsa
Model
Generalized Logit
Optimization Technique
Newton-Raphson
Variance Adjustment
Degrees of Freedom
(DF)
Any inpatient admissions - past
year
weightsa
Variance Estimation
Method
Taylor Series
Variance Adjustment
Degrees of Freedom
(DF)
Number of Observations Read
1825
Number of Observations Used
1825
Sum of Weights Read
7955
Sum of Weights Used
7955
Response Profile
Ordered
Value ANYIP
Total
Frequency
1 no/na/
dk
2 yes
Total
Weight
1028 4552.607
1
797 3402.392
9
Logits modeled use ANYIP='yes' as the reference category.
Model Convergence Status
Convergence criterion (GCONV=1E-8)
satisfied.
Model Fit Statistics
Criterion
AIC
Intercept
Intercept
and
Only Covariates
10863.07
8
10846.426
19
V.A.Morgan document1
15 March 2016
Model Fit Statistics
Criterion
Intercept
Intercept
and
Only Covariates
SC
10868.58
7
10857.445
-2 Log L
10861.07
8
10842.426
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square DF
Pr > ChiSq
Likelihood Ratio
18.6517
1
<.0001
Score
18.6230
1
<.0001
Wald
3.8805
1
0.0489
Type 3 Analysis of Effects
Effect
AGE_CALC
DF
Wald
Chi-Square
Pr > ChiSq
1
3.8805
0.0489
Analysis of Maximum Likelihood Estimates
Parameter
ANYIP
Intercept
no/na/dk
1
AGE_CALC
no/na/dk
1
Standard
Error
Wald
Chi-Square
Pr > ChiSq
-0.0568
0.1786
0.1011
0.7506
0.00884
0.00449
3.8805
0.0489
DF Estimate
Odds Ratio Estimates
Effect
ANYIP
AGE_CALC
no/na/dk
Point
Estimate
1.009
95% Wald
Confidence Limits
1.000
1.018
Association of Predicted Probabilities and
Observed Responses
Percent Concordant
51.8 Somers' D
0.06
2
Percent Discordant
45.7 Gamma
0.06
3
2.5 Tau-a
0.03
0
Percent Tied
Pairs
81931 c
6
0.53
1
20