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Malimu case control studies

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Malimu case control studies

  1. 1. Introduction to Case Control Studies 1 Malimu MSc.Epidemiology PhD. Candi. Department of Epidemiology and Biostatistics, MUHAS./KIU
  2. 2. Learning Objectives When you have completed this session you will be able to:  Describe the characteristics of a case control study  List the type of bias most likely to affect a case control study  List the conditions under which a case control study is an appropriate choice to address a research question  Define the term “control group” and list the characteristics of a good control group 2
  3. 3. 3 Case-control Cohort Individuals Intervention Retrospective Prospective Descriptive Populations Analytical Observational Case-series Cross-sectional Ecologic Clinical trials Epidemiological studies
  4. 4. Intro to Case-control studies  Case-control studies provide insight on the aetiology of many condition  Prone for bias in selecting cases/controls, eliciting exposure status and sophisticated nature of its analysis  Hallmark of a professional epidemiologist 4
  5. 5. 5 Disease No disease Exposure ? ? Retrospective Nature Case-Control Study (Case) (Control)
  6. 6. Design of a Case-control Study
  7. 7. Variants of case-control design  Case- control study (Classical)  Case-cohort studies  Case-only studies  Case-crossover studies 7
  8. 8. Major Steps in case-control study  Define and select cases  Select controls  Ascertain exposures  Compare exposure in cases and controls  proportions/odds ratios ....  Test any differences for statistical significance 8
  9. 9. Observation  Start with cases  Are any observed exposures higher than expected ?  To find this out we need a comparison group  This group are known as controls 9
  10. 10. Selection of cases  Cases should be selected independent of the exposure  Not necessarily represent all people with the disease but controls should be from the same population as the cases  Incident cases better than prevalent cases 10
  11. 11. Who is the Right Control?  As similar to a case as possible but without the disease in question  Selected from the same population or study base as cases  Must have the same opportunity for exposure as a case  Must be subject to the same inclusion and exclusion criteria  No one control group is optimal for all situations
  12. 12. Examples of Controls  Population control  Neighbourhood  Hospitals /clinic based-control  Friends 12
  13. 13. General Population Controls  Advantages  Exposure in the control estimate that of the population  Direct calculation of risk  Inferences are easy  Include health people  Disadvantages  Cost  Sampling frame 13
  14. 14. Neighborhood Controls  Advantages:  Inexpensive,efficient  Matched for potentially confounding variables  Disadvantages  Exposure related to neighborhood 14
  15. 15. Hospital Controls  Advantages:  Convenient  Easily identified  More likely to comply, interviewed and tested  Same selection procedure as cases  Disadvantages: -Not source population for the cases (Berksons bias) -May have diagnosis interfering with outcome -Generalizability problems 15
  16. 16. Friend Controls  Advantages  Convenient  Include health and cooperative people  Disadvantages  Friends may share same exposure (over-matching) -Overlapping of friendships 16
  17. 17. Number of controls  Availability  Ratio controls / cases  Trade-off: cost vs. power  Decision based on power calculation  More than one control group? 17
  18. 18. Biases in Case-control studies  Selection biases  Cases selection –external validity  Controls selection-internal validity  Measurement biases  Observer bias-favour cases  Recall bias-cases remembers more  Information bias –equal collection methods  Confounders 18
  19. 19. Matching in Case –control studies  It is a strategy for controlling potential confounders.  Each case is matched with a control in a number of variables E.g. Age, sex, education…  Advantages  Help to control un-measurable confounders eg. Genetic-using siblings  Eliminate the need to list all possible controls  Increase precision of odds ratio by weakening the assoc. btwn confounder and outcome 19
  20. 20. Comparison of cases and control  Comparison of occurrence of disease/event is made between the exposed and unexposed  Odds ratio (OR) is the measure of effect  OR= odds of exposure among the cases odds of exposure among the controls 20
  21. 21. Cases Controls Exposed a b Not exposed c d Total a + c b + d Odds ratio = (a/c)/ (b/d) = a x d b x c Distribution of cases and controls according to exposure in a case control study
  22. 22. 22 Oral Myocardial contraceptives Infarction Controls Yes 693 320 No 307 680 Total 1000 1000 Odds ratio = 693 x680 = 4.79 320 x 307 Distribution of myocardial infarction cases and controls by oral contraceptive use
  23. 23. Odds Ratio: Interpretation  OR > 1 - the risk of disease in the exposed group is greater than the risk in the unexposed group  OR = 1 - the risk of disease is the same in the exposed and unexposed (no association)  OR < 1 - the risk of disease in the exposed group is less than the risk in the unexposed
  24. 24. Advantages of Case Control Studies  Rare diseases  Multiple exposures  Diseases with long latent periods  Small sample size  Low cost  Secondary data analysis possible 24
  25. 25. Disadvantages of Case-control Studies  Limited to one outcome variable  Selective survival effect  Selection of controls difficult  Not suitable for rare exposures  Nonrepresenativeness of cases (Berkson’s fallacy)!!!!  Problems with recall (information bias) 25

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