1. Chihaya Koriyama August 17 (Lecture 1)
Case-control study
Chihaya Koriyama
August 17 (Lecture 1)

2. Study design in epidemiology
Observational study
individual
Case-control
study
Cohort study
population
Ecological
intervention

3. Why case-control study?
In a cohort study, you need a large number of the subjects to obtain a sufficient number of case, especially if you are interested in a rare disease.
Gastric cancer incidence in Japanese male:
128.5 / 100,000 person year
A case-control study is more efficient in terms of study operation, time, and cost.

4. Case-control study - subjects
Start with identifying the cases of your research interest.
If you can identify the cases systematically, such as a cancer registration, that would be better.
Incident cases (newly diagnosed cases) are better than prevalent cases (=survivors).
Recruitment of appropriate controls
From residents, patients with other disease(s), cohort members who do not develop the disease yet.

5. Various types of case-control studies
１）a population-based case-control study
Both cases and controls are recruited from the population.
２）a case-control study nested in a cohort
Both case and controls are members of the cohort.
３）a hospital-based case-control study
Both case and controls are patients who are hospitalized or outpatients.

6. Who will be controls? Control ≠ non-case
Controls are also at risk of the disease in his(her) future.
In a case-control study of gastric cancer, a person who has received the gastrectomy cannot be a control.
In a case-control study of car accident, a person who does not drive a car cannot be a control.

7. Case-control study - information
Collection of the information (past information) by interview, biomarkers, or medical records
Exposure (your main interest)
Potential confounding factors
Bias & Confounding
Selection bias
Information bias (recall bias)
confounding

8. Selection bias
Sampling is required in a case-control study (since we cannot examine all!)
We need to chose appropriate subjects.
Selection bias is “Selection of cases and controls in a way that is related to exposure leads to distortions of exposure prevalence”.

9. Error & Bias Error: random error Bias：systematic error
differential misclassification
non-differential misclassification
This is a problem!

10. An example of non-differential misclassification in an exposure variable
We want to compare mean of blood pressure levels between cases and controls.
The blood pressure checker has a problem and always gives 5mmHg-higher than true values.
All subjects were examined by the same blood pressure checker.
→　no problem for internal comparison

11. Observed risk estimate always comes close to “1(null)”
An example of non-differential misclassification in the ascertainment of exposure
Case
Control
Odds ratio
True (nobody knows)
Exp + 50
Exp - 10 90
Results of test＊ 41 49 19 91 1 10 9 10
(50*90) /
(50*10) =9
(41*91) /
(49*19)=4.01
＊Sensitivity 80% (80% of the exposed subjects are correctly
diagnosed)
Specificity 90% (90% of the un-exposed subjects are correctly diagnosed)

12. Differential misclassification
Selection bias
Detection bias
Information bias
Recall bias
Family information bias

13. Confounding Confounders are risk factors for the outcome.
Confounders are related to exposure of your interest.
Confounders are NOT in the process of causal relationship between the exposure and the outcome of your interest.

14. Example of confounder - living in a HBRA is a confounder -
HBRA: high background radiation area
Low socio-economical
status in HBRA
A surrogate marker of low socio-economic status
High infant death
Living in a
HBRA
Causation ?
Exposure to
radiation in uterus

15. Example of confounder - smoking is a confounder -
Smoking is a risk factor of MI
Myocardial
infarction
Causation ?
(We observe an association)
smoking
Radiation
related by chance

16. Example of “not” confounder - pineal hormone is not a confounder -
EMF: electro-magnetic field
Decrease of pineal hormone
may be the risk of breast ca.
Breast cancer
Down regulation
of pineal hormone
Causation ?
電磁場の乳がんに対する影響が全て松果体ホルモン低下で説明できるなら、松果体ホルモンレベルは交絡因子として取り扱うべきでない
EMF
EMF exposure induces down
regulation of pineal hormone
If EMF exposure cause breast cancer only through down regulation of
pineal hormone, this is not a confounder.

17. Why do we have to consider confounding?
We want to know the “real” causal association but a distorted relationship remains if you do not adjust for the effects of confounding factors.

18. How can we solve the problem of confounding?
“Prevention” at study design
Limitation
Randomization in an intervention study
Matching in a cohort study But not in a case-control study

19. How can we solve the problem of confounding?
“Treatment “ at statistical analysis
Stratification by a confounder
Multivariate analysis

20. Case ascertainment Who is your case?
Patient?
Deceased person?
What is the definition of the case?
Cancer (clinically? Pathologically?)
Virus carriers (Asymptomatic patients)
→ You need to screen the antibody

21. Incident or Prevalent cases with chronic disease(s)
Incident case
Prevalent case
You recruit cases prospectively.
Newly diagnosed cases
All cases are alive.
You recruit cases cross-sectionaly.
Mixed cases with diagnosed recently and long time ago.
You miss patients who died before study.
Only survivors
Cases with better prognosis!

22. Matching in a case-control study
Matched by confounding factor(s)
Sex, age ・・・・
Cannot control confounding
Conditional logistic analysis is required.
To increase the efficiency of statistical analysis

23. Over matching
Matched by factor(s) strongly related to the exposure which is your main interest
CANNOT see the difference in the exposure status between cases and controls

24. ↓ ↓ a case-control study Cases Controls (brain tumor) N=100 N=100
Mobile phone users (NOT recently started)
↓ ↓  
The incubation period of tumor is a few years at least.
Cases
Controls
Yes 50 10 No 90

25. Risk measure in a case-control study
Odds = prevalence / (1－ prevalence)
Odds ratio = odds in cases / odds in controls
Disease
+（case） －（control）
+ a c
Exposure － b d
Exposure odds in cases ＝a / b
Exposure odds in controls＝c / d
Odds ratio＝(a / b) / (c / d) ＝ a * d / b * c

26. Comparison of the study design
Case-control Cohort
Rare diseases suitable not suitable
Number of disease <
Sample size relatively small need to be large
Control selection difficult easier
Study period relatively short long
Recall bias yes no
Risk difference no available available