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Case-control study

Case-control study. Chihaya Koriyama August 17 (Lecture 1). Study design in epidemiology. 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.

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Case-control study

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

  2. Study design in epidemiology

  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：systematicerror • 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 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 ? 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 cross-sectionaly. Mixed cases with diagnosed recently and long time ago. You miss patients who died before study. Only survivors • You recruit cases prospectively. • Newly diagnosed cases • All cases are alive. 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) ↓ ↓ 50 10  The incubation period of tumor is a few years at least.

  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 1 1< Sample size relatively small need to be large Control selection difficult easier Study period relatively short long Recall bias yes no Risk difference no availableavailable

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