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 study 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. 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. Various types of case-control studies

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. An example of non-differential misclassification in the ascertainment of exposure CaseControlOdds ratio True (nobody knows) Exp +50 Exp -1090 Results of test ＊ Exp +4149 Exp -1991 ＊ Sensitivity 80% (80% of the exposed subjects are correctly diagnosed) Specificity 90% (90% of the un-exposed subjects are correctly diagnosed) (50*90) / (50*10) =9 (41*91) / (49*19)=4.01 Observed risk estimate always comes close to “1(null)” 10 19

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

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

16. Example of “not” confounder - pineal hormone is not a confounder - Breast cancer Down regulation of pineal hormone Causation ? EMF EMF: electro-magnetic field EMF exposure induces down regulation of pineal hormone Decrease of pineal hormone may be the risk of breast ca. 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 You recruit cases prospectively. Newly diagnosed cases All cases are alive. Prevalent case 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. CasesControls (brain tumor)N=100 Mobile phone users (NOT recently started) ↓ 50 10 a case-control study The incubation period of tumor is a few years at least. CasesControls Yes5010 No5090

25. Risk measure in a case-control study Odds = prevalence / (1 － prevalence) Odds ratio = odds in cases / odds in controls Disease + （ case ） －（ control ） +ac Exposure － bd 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-controlCohort Rare diseases suitable not suitable Number of disease1 1< Sample sizerelatively small need to be large Control selection difficult easier Study periodrelatively short long Recall bias yes no Risk difference no available available