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Principles of case control studies

This presentation discusses the importance of matching in case control studies, including the types of matching, how to control confounding factors, and the advantages and disadvantages of matching.

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Principles of case control studies

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  1. Principles of case control studies • Part III • Matching Many slides in this presentation are from the World Health Organization and the European Programme for Intervention Epidemiology Training, thank you. Piyanit Tharmaphornpilas MD, MPH The International Field Epidemiology Training Program, Thailand

  2. Sunbathe Diabetes mellitus Age is confounding factor! need to be controlled Reality : Age Sunbathe Diabetes mellitus Confounding Hypothesis: Sunbathe is a risk factor for being diabetes mellitus

  3. How to control confounding factors • Randomisation • Restriction • Matching • Adjustment • Mutivariate analysis

  4. Age is confounding factor! need to be controlled Because age is confounding factor, so (In cohort study) Age of exposed and unexposed population should be comparable! Then, effect of age on the study association will be taken off. (In case-control) age of cases and controls should be comparable! If a case ages 30, his control should age 30 too. Reality : Age Sunbathe Diabetes mellitus

  5. Types of matching • Frequency matching Large strata: Controls are selected in proportion to the number of cases in each strata of the matching variable • Individual matching Small strata : For each case one or more controls are selected with the matching characteristics

  6. Frequency matching Controls are selected in proportion (%) to the number of cases in each strata of the matching variable Cases 30 30 20 10 10 100 Controls 60 60 40 20 20 200 Age 15-24 25-34 35-44 45-54 >54 Total The distribution of cases and controls is similar for age, and controls are no more representative of the not-ill population for age

  7. Individual matching For each case one or more controls are selected with the matching characteristics No. Case Control1 Control2 1 age 30 age 30 ฑ 5 age 30 ฑ 5 2 age 20 age 20 ฑ 5 age 20 ฑ 5 3 age 10 age 10 ฑ 5 age 10 ฑ 5 The distribution of cases and controls is similar for age, and controls are no more representative of the not-ill population for age

  8. Matching : analysis If…. control enrolment is done by matching Then…. analysis should be adjusted for it (by strata)

  9. Adjustment by Mantel-Haenszel Using confounding (matching) variable as strata S [(ai.di) / Ti] S [(bi.ci) / Ti] OR M-H=

  10. Frequency matching : analysis S [(ai.di) / Ti] S [(bi.ci) / Ti] OR M-H = • Stratified analysis on the frequency matching variable • Mantel Haenszel weigthed OR • ExposureCases Controls Total • Strata 1 • yes ai bi L1i • no ci di L0i • Total C1i C0i Ti • Strata j ....

  11. Individual matching analysis Controls Exposed Not Exposed C+/Ctr + C+/Ctr - Exposed Cases C-/Ctr + C-/Ctr - Not Exposed Pairs of cases and controls

  12. Individual matching analysis Controls Exposed Not Exposed e f Exposed Cases g h Not Exposed Pairs of cases and controls

  13. ControlsExposedNot exposedTotal Exposede f a Not exposedg h c Total b d T Odds ratio: f/g C A S E S

  14. Atherosclerosis risk in Communities study Association between CMV infection and Carotid Atherosclerosis Controls CMV+ CMV- CMV+ 214 65 Atherosclerosis 42 19 CMV- Cases and controls individually match paired by Age group, sex, ethnicity, field center and date of exam From: PD Sorlie et al, cytomegalovirus and carotid Atherosclerosis, Journal of Medical Virology, Vo 42, pp 33-37,1994

  15. We cannot analyze a matched case-control study by unmatched method Why? ? Because matching process introduce selection bias This selection bias is controllable by stratified analysis

  16. Matching : advantages • When there is a potentially strong confounding variable • Tends to increase the statistical power • Logistically straightforward way to obtain a comparable control group

  17. Matching: disadvantages • Difficult to find a matched control • Cannot assess the association between matching variables and outcome • Implies some tailoring of the selection of study groups to make them comparable (less representativeness) • Once is done cannot undone, risk of overmatching • No statistical power is gained if the matched variables are weak confounders

  18. Don’t match (too much) End of part III

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