1. Case-Control Studies

2. Historical View Historically considered a weak design
Nested case-control studies are a logical extension of cohort studies and an efficient way to learn about associations.
Design of nested studies is as strong as cohort studies.

3. General Definition: Case-Control Study
A method of sampling a population in which:
cases are identified and enrolled
a sample of the population that produced the cases is identified and enrolled
Exposures are determined

4. When is it desirable to conduct a case-control study?
When exposure data are expensive or difficult to obtain
- Ex: Pesticide study, biomarker
When disease has long induction and latent period
- Ex: Cancer, cardiovascular disease
When the disease is rare
Ex: Studying risk factors for birth defects

5. When is it desirable to conduct a case-control study?
When little is known about the disease
Ex. Early studies of AIDS
When underlying population is dynamic
Ex: Studying breast cancer on Cape Cod

6. Cases What do you need to think about in identifying cases.
source population – catchment area/characteristics
case identification/ascertainment
case definition

7. Controls
Definition: A sample of the source population that gave rise to the cases.
Purpose: To estimate the exposure distribution in the source population that produced the cases.

8. Selecting Controls General population controls
Most often used when cases are selected from a defined geographic population
Sources: random digit dialing, residence lists, drivers’ license records

9. General Population Advantages Disadvantages
Because of selection process, investigator is usually assured that they come from the same base population as the cases.
Disadvantages
Need accurate sampling frame
Time consuming, expensive, hard to contact and get cooperation
May remember exposures differently than cases

10. Hospital Controls
Used most often when cases are selected from a hospital population
Example: Study of cigarette smoking and myocardial infarction among women.
Cases identified from admissions to hospital coronary care units.
Controls drawn from surgical, orthopedic, and medical unit of same hospital.
Controls included patients with musculoskeletal and abdominal disease, trauma, and other non-coronary conditions.

11. Advantages of Hospital Controls
Same selection factors that led cases to hospital led controls to hospital
Is this always true?
Easily identifiable and accessible (so less expensive than population-based controls)
Accuracy of exposure recall comparable to that of cases since controls are also sick
More willing to participate than population-based controls

12. Disadvantages of Hospital Controls
Since hospital based controls are ill, they may not accurately represent the exposure history in the population that produced the cases
Hospital catchment areas may be different for different diseases

13. What Illnesses Make Good Hospital Controls?
Those illnesses that have no relation to the risk factor(s) under study
Example: Should respiratory diseases be used as controls for a study of smoking and myocardial infarction?
Do they represent the distribution of smoking in the entire population that gave rise to the cases of MI?

14. Special Control Groups
Friends, spouses, siblings, and deceased individuals.
These special controls are rarely used.
Cases may not be able to nominate controls because they have few appropriate friends, are widowed or are only or adopted children.
Dead controls are tricky to use because they are more likely than living controls to smoke and drink.
For rare diseases you don’t want to lose cases because you can’t identify a control

15. Example
Hypothesis: Pesticide exposure increases the risk of breast cancer.
Consider a hypothetical prospective cohort study of 89,949 women aged 34-59
1,439 breast cancer cases identified over 8 years of follow-up
Blood drawn on all 89,949 at beginning of follow-up and frozen
Exposure: Level of pesticides (e.g. DDE) in blood characterized as high or low

16. Cohort: Results
Breast Cancer
Yes No
Total
High
360
13,276
13,636
Low
1,079
75,234
76,313
1,439
88,510
89,949
Pesticide
Relative Risk = RR = (360/13,636) / (1,079/76,313) = 1.9

17. Cases
Criteria for case definition should lead to accurate classification of disease
Efficient and accurate sources should be used to identify cases: existing registries, hospitals
What do the cases give you? Think of the standard 2 X 2 table:
Disease
Yes (case) No
Total
Yes
a - 360 ? c
a+c
Exposed

18. Rate of disease in exposed: a/? Rate of disease in unexposed: c/?
Cases give you the numerators of the rates of disease in exposed and unexposed groups being compared:
Rate of disease in exposed: a/?
Rate of disease in unexposed: c/?
What is missing?
If this were a cohort study, you would have
total population
total person-years
for both the exposed and non exposed groups
provide the denominators for the compared rates.

19. Where do you get the information for the denominators in a case control study?
THE CONTROLS.
A case-control study can be considered a more efficient form of a cohort study.
Cases are the same as those that would be included in a cohort study.
Controls provide a fast and inexpensive means of obtaining the exposure experience in the population that gave rise to the cases.

20. Introduction
Practical Problem: Quantifying pesticide levels in the blood is very expensive; it's not practical to analyze all 89, 949 blood samples
analyze blood on all cases (N=1,439)
sample of the women who did not get breast cancer, say two times as many cases (N=2,878)
Nested Case-Control Study

21. Example: Pesticide Study
These data can be used to estimate the relative risk.
We have just identified cases of disease from a defined population, and then taken a sample of that population for comparison.
Exposure histories are determined.
Breast Cancer
Yes No
High
360
432
Low
1,079
2,446
Total
1,439
2,878
DDE

22. Important consideration for selecting controls: “the would criterion”
Review: Controls are a sample of the source population that gave rise to the cases.
Purpose is to provide information on the exposure distribution in the source population.
When selecting a control group consider the “WOULD CRITERION”: If a member of the control group actually had the disease under study WOULD he/she end up as a case in your study?
Answer should be YES.

23. Analysis of Case-Control Studies
Cases
Controls
Total Population
Yes a b ? No c d
Exposed
Controls are a sample of the population.
Can you get an estimate of disease incidence using this study design?
Can you estimate risk of disease?

24. Analysis of Case-Control Studies
Definition of odds: the ratio of the probability of an event occurring to that of it not occurring
Example: Probability of getting a heads on one coin toss = ½ = .50. Probability of NOT getting a heads on one coin toss = ½ = .50. Odds of getting a heads on a coin toss = .5/.5 = 1:1

25. Analysis of Case-Control Studies
Two possible outcomes for an exposed person:
case or not Odds=a/b
Two possible outcomes for an unexposed person:
case or not Odds=c/d
Odds ratio = odds of an exposed person being a case/odds of an unexposed person being a case
Odds ratio = a/b/c/d = ad/bc
Just like the incidence rate ratio and cumulative incidence ratio, the odds ratio is a ratio measure of association.

26. Example: Pesticide Study
Calculate the Odds Ratio
Breast Cancer
Yes No
High
360
432
Low
1,079
2,446
Total
1,439
2,878
DDE

27. Example: Pesticide Study
Calculate the Odds Ratio
Odds of being exposed in cases
Odds of being exposed in controls
Odds of being a case among exposed
Odds of being a case among the unexposed
Breast Cancer
Yes No
High
360
432
Low
1,079
2,446
Total
1,439
2,878
DDE

28. Strengths case-control studies
Efficient for rare diseases and diseases with long induction and latent period.
Can evaluate many risk factors for the same disease so good for diseases about which little is known

29. Weaknesses of case-control studies
Inefficient for rare exposures
Vulnerable to bias because of retrospective nature of study
May have poor information on exposure because retrospective
Difficult to infer temporal relationship between exposure and disease
How do these strengths and weaknesses compare to cohort studies?