Variants
of the case- control design
Katharina Alpers
EPIET introductory course, Menorca (Spain),
10 October, 2011
Overview
Design of case-control studies
• Exclusive („traditional“)
• Inclusive („case-cohort“)
• Concurrent (density)
• Case-to-case
• Case-crossover
2
Cohort study: incidence risk
Exposure
Total
Cases
Risk (%)
Risk ratio
Exposed
100
40
40%
4
Unexposed
100
10
10%
Reference
Total
200
50
25%
Cumulative incidence
Number of cases/population initially at risk
3
Cohort study: incidence rate
Exposure
Total
Time
Cases
Rate
per 100 p.y.
Rate ratio
Exposed
1500 p.y
40
2.7/100 p.y.
2.7
Unexposed
1000 p.y.
10
1.0/100 p.y.
Reference
Total
2500 p.y.
50
2.0/100 p.y.
Incidence density
Number of cases/sum of times at risk
4
Cohort study
Exposed population (E)
Initially
at
Risk
Ne
Cases exposed
Ce
Person years at risk
of exposed (pyare)
Currently at risk
Still at risk
Ne - Ce
Unexposed population (U)
Initially
at
Risk
Nu
Cases unexposed
CU
Person years at risk
of unexposed (pyaru)
Start of study
Rodrigues L et al. Int J Epidemiol. 1990;19:205-13.
Currently at risk
Still at risk
Nu - Cu
End of study
Time
5
Case-control studies
• Efficient for rare diseases
• Compare exposure in cases to sample of population
– sampled from source population that gives rise to
cases
– representative of exposure in source population
• Sampling independent of exposure status
• Different control sampling schemes
6
Traditional case-control design (exclusive)
Exposed population (E)
Cohort study
Cases exposed
Cases
Still at risk
Unexposed population (U)
Cases unexposed
Sample of “non cases”
Still at risk
End of study
7
Traditional design
• Controls sampled from population still at risk at the end
of the study period
• Disease odds ratio = exposure odds ratio
• If disease is rare:
OR good estimate of risk ratio and rate ratio
8
Inclusive design: Case cohort study
Exposed population (E)
Cohort study
Cases exposed
Cases
Still at risk
Unexposed population (U)
Cases unexposed
Sample of source population
Still at risk
End of study
9
Case-cohort design
• Control group estimates the proportion of the total
population that is exposed
• Controls selected from all individuals at risk at the start
of the study
– sampled regardless whether or not they will fall ill
• Case may also be selected as a control and vice versa
-> kept in both groups
• OR estimates relative risk
10
Concurrent design: density case control
Exposed population (E)
Cohort study
Cases exposed
Cases
Still at risk
Unexposed population (U)
Cases unexposed
Sample of source population
Still at risk
Still at risk
End of study
11
Concurrent design: density case control
• Controls selected from those still at risk when a case
occurs
• Control can later become a case
• Not vice versa -> cases no longer at risk
• Controls who later become cases kept in both groups
• Controls represent person years at risk experience
among exposed and unexposed
• Matched analysis on time of selection
• OR estimates the rate ratio
12
How to select controls to estimate the
respective measure of association
Measure
Design
Risk ratio
Inclusive
Rate ratio
Concurrent
Odds ratio
Exclusive
Formulation
Alternative
formulation
Controls to be
sampled from
Ce/Ne
Cu/Nu
Ce/Cu
Ne/Nu
Total study population
regardless of past or
future disease status
Ce/pyare
Cu/pyaru
Ce/Cu
pyare/pyaru
People currently at risk
Ce/(Ne- Ce)
Cu/(Nu- Cu)
Ce/Cu
(Ne- Ce) /(Nu- Cu)
People disease-free
throughout study
period
13
Rodrigues L et al. Int J Epidemiol. 1990;19:205-13.
What design and when?
• Rare diseases: all give similar results
• Non-recurrent disease with high incidence
-> Case cohort design (inclusive): OR  relative Risk
• Recurrent common disease
-> Density case control design (concurrent):
OR  relative Rate
• Probability or effect of exposure changes along time
-> Density case control design: OR  relative Rate
• No need to quantify
-> traditional design
14
Relationship between OR and RR,
according to the primary attack rate (AR)
Acknowledgements:
Olivier le Polain, EPIET Cohort 15
HPA London Epidemiology Unit, UK
15
Cases detected by surveillance systems
• Non-random selection process:
– Host factors (eg. asymptomatic infections)
– Different health care seeking behaviour
– Incomplete lab investigation
– Incomplete reporting
• Differential recall
– Between reported and not reported cases
– Between cases and controls
16
Case-to-case approach
• Same disease, different subtypes/clones:
– Serotypes
– Phage types
– Antibiotic resistance patterns
• Controls = cases with non epidemic subtypes
–
–
–
–
from same source population
same susceptibility (underlying diseases)
included as cases if they had the outbreak strain
readily available
• Reduces selection AND recall bias
• Food-exposure collected before status is known
17
Two listeriosis outbreaks France, 1999-2000:
two distinct PFGE patterns
Cases
10
9
Outbreak 2 (32 cases)
October
November
1999
8
6
4
2
52
50
48
46
44
Outbreak 1 (10 cases)
42
40
8
7
6
5
4
3
2
1
0
December
January
February March
2000
de Valk H et al. Am J Epidemiol 2001;154:944-50
18
Listeriosis outbreak cases and sporadic cases
distinguished by routine PFGE, France, 1999-2000
Cases
14
Sporadic cases
12
Outbreak 2 (32 cases)
10
Outbreak 1 (10 cases)
8
6
4
2
October
November
December
1999
de Valk H et al. Am J Epidemiol 2001;154:944-50
January
2000
8
6
4
2
52
50
48
46
44
42
40
0
February
March
19
Case to case control study:
controls selected among sporadic cases
listeriosis outbreak, France, 1999-2000
Cases
14
12
10
Other sporadic cases
Sporadic cases used as controls (N = 32)
Outbreak 2 (N = 32)
Outbreak 1 (N = 10)
8
6
4
2
December
8
6
4
2
52
November
1999
50
48
46
October
44
42
40
0
January
February March
2000
20
de Valk H et al. Am J Epidemiol 2001;154:944-50
Outbreak of listeriosis, France,
December 1999 - February 2000
Results multivariable analysis
(29 cases, 32 controls)
Food consumed
Pork tongue in jelly
Cooked ham
Pâté de campagne
Adjusted
Odds ratio*
95% CI
p
75.5
7.1
8.9
4.7 – 1216.0
0.7 – 71.8
1.7 – 46.1
0.002
0.1
0.009
*adjusted for underlying condition, pregnancy status and date of interview
de Valk H et al. Am J Epidemiol 2001;154:944-50
21
Case-crossover design
• Same person taken as its own control
-> No between-persons confounding
• Matched design:
– Compare exposure in a risk period to one or more control periods
– Only pairs of discordant periods used in the analysis
• Acute diseases
• Exposure
– must vary over time
– short induction and transient effect
• sensitive to recall bias
22
Case-crossover design
Reference
period
“Wash out”
Current
period
period
Cases
Matched pairs
1
Discordant 0, 1
2
Discordant 1, 0
3
Concordant 1, 1
4
Concordant 0,0
Exposure
Study
Prolonged Salmonella Typhimurium outbreak, France
Food exposures in the risk and control period
and matched OR for 17 nosocomial cases
Foods
Risk
period
Exposed (%)
Veal
Pork
Hamburgers
Ham
Pâté
Chicken
Turkey
“Cordon bleu”
Lamb sausages
Poultry sausages
5 (29)
4 (23)
13 (77)
6 (35)
2 (12)
2 (12)
11 (65)
0 (0)
2 (12)
2 (12)
Control
period
Matched
OR
Exposed (%)
1 (6)
6 (35)
5 (29)
5 (29)
2 (12)
3 (18)
6 (35)
2 (12)
0 (0)
0 (0)
Haegebaert S et al. Epidemiol infect 2003;130,1-5
5
0,6
5
1,5
1
1
2,67
undefined
undefined
undefined
95%
C.I.
0,6 - 236,5
0,1 - 3,1
1,1 - 46,9
0,2 - 17,9
0,01 - 78,5
0,01 - 78,5
0,7 - 15,6
24
Time trend in exposure:
Between period confounding
Case-time controls:
ORb for the time trend
Cases:
ORa for the exposure
and the time trend
Control period
Risk period
onset
Case-time control design
ORa/ORb = OR of exposure adjusted for time trend
25
Folic acid antagonists (FAA) in pregnancy and
congenital cardiovascular defects (CCD)
•
•
•
•
Case-crossover approach
Case: Woman who had a child with CCD (N=3870)
Exposure: FAA during 2nd & 3rd month of pregnancy
Control: Woman who had a child without CCD (N=8387)
Delivery
Cases:
-2
-1
1
Control
period
Controls:
-2
-1
2
3
4
5
6
7
2
3
9
OR=1.0 (0.5-2.0)
Case-time control
OR = 1/0.3 = 2.9 (1.2-7.2)
Risk
period
1
8
4
5
Hernandez-Diaz S. Am J Epidemiol 2003;158:385-391
6
7
8
9
OR= 0.3 (0.2-0.6)
26
Conclusions
• If you do not need that OR estimates correctly the RR
-> “traditional design”
• Otherwise, if you need OR  RR
-> identify the best design for each situation
• If it is difficult to find appropriate controls
– Case to case
– Case-crossover
27
References
•
•
•
•
•
•
•
Rodrigues L et al. Int J Epidemiol 1990;19:205-13
Rothman KJ. Epidemiology: an introduction. Oxford University Press 2002, 73-93
Rothman KJ, Greenland S, Lash TL: Modern Epidemiology. 3. ed., Philadelphia:
Lippincott Williams & Wilkins, 2008. Chapter 8: Case-Control Studies, 111-127
McCarthy N, Giesecke J. 1 Int J Epidemiology 1999; 28, 764-8
de Valk H et al. Am J Epidemiol 2001;154:944-50
Haegebaert S et al. Epidemiol infect 2003;130,1-5
Hernandez-Diaz S et al. Am J Epidemiol 2003;158:385-391
Further Reading
•
•
•
•
Suissa S. The case-time-control design. Epidemiology 1995;6:248-253.
Greenland S. Epidemiology. 1996; 7231-239.
Mittleman, Maclure, Robins. Am J Epidemiol 1995;142;1:91-98.
Karagiannis I et.al. Epidemiol Infect 2010;138, 1726-1734
28