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A Brief Introduction to Epidemiology - IX (Epidemiologic Research Designs: Case-Control Studies)

Learn the basics of case-control studies in epidemiology, including methodology, pros, and cons, through examples and comparisons to other study designs. Gain insights on association strength and practical considerations.

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A Brief Introduction to Epidemiology - IX (Epidemiologic Research Designs: Case-Control Studies)

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  1. A Brief Introduction to Epidemiology - IX(Epidemiologic Research Designs: Case-Control Studies) Betty C. Jung, RN, MPH, CHES

  2. Learning/Performance Objectives • To develop an understanding of: • What case-control studies are • The value of such studies • The basic methodology • Pros and Cons of such studies

  3. Introduction Epidemiology studies the distribution of disease in a number of ways. The two major categories of epidemiological studies are: Observational and experimental studies. Most epidemiological studies are observational.

  4. Epidemiological Study Designs • Observational Studies - examine associations between risk factors and outcomes (Analytical - determinants and risk of disease, and descriptive - patterns and frequency of disease) • Intervention Studies - explore the association between interventions and outcomes. (Experimental studies or clinical trials)

  5. Research Designs in Analytic Epidemiology • Ecologic Designs Cross-Sectional Study • Case-Control Study • Cohort Study

  6. Case-Control Studies • “Flashback Studies” (Paffenbarger, 1988) • Retrospective - compare cases and controls for presence of disease • Includes passage of time. • Historical - assess past characteristics or exposures in two groups of people- cases and controls

  7. Examples • The relationship between thalidomide and unusual limb defects in Germany • The relationship between meat consumption and enteritis necroticans in Papua New Guinea

  8. Value • Simple to conduct • Cost-effective way to study a rare disease

  9. Case-Control Studies: Methodology First Select the Cases and Controls Cases Control (without disease) (with disease) Then measure, post-exposure Were Exposed A B D C Not Exposed A+C B+D Population Exposed A/A+C B./B+D

  10. Case Control Design Time Direction of Inquiry Cases with the Disease Exposed Population Not Exposed Controls without the disease Exposed Not Exposed

  11. Case-Control Studies • Cases - Has condition or health outcome of interest. Has higher frequency or greater degree of exposure than non-cases. • Controls (non-cases) - Does not have the health condition. Serves as the comparison group • Ask about history of contact with or exposure to supposed causes

  12. Case-Control Studies • If controls are well chosen, the only antecedent difference will be in the level of a characteristic that is related causally to the development of a disease (I.e, exposure to a chemical resulted in cancer). • Quantify with odds ratios

  13. Strength of Association Relative Risk;(Prevalence);Odds RatioStrength of Association 0.83-1.001.0-1.2None 0.67-0.831.2-1.5 Weak 0.33-0.671.5-3.0 Moderate 0.10-0.333.0-10.00Strong <0.01 >10.0Approaching Infinity

  14. Methodology Issue: Matching • Matching - control for confounding variables. If you do not match then control by subject selection (study only males to eliminate gender as a confounding variable) • Matching • Subject selection • Statistical control during data analysis • The more variables that need to be matched the greater the universe we need. • Problem - match age, sex and SES - Control must be the same.

  15. Methodological Weaknesses • Biased reporting of the antecedent (having lung CA -> patients over reporting smoking (from guilt, knowledge or selective memory) • Subject selection (decreases with cases and controls in the same facility) • Limited to only cases, who have survived at the same time. Selective survival

  16. Pros • Cases easily available • Good for less common or rare cases • Quick, inexpensive • Can be conducted by clinicians in clinical facilities • Tend to support, not prove causal hypothesis by establishing associations • Historical data available in clinical records • Number of subjects needed is small

  17. Cons • Info about antecedents depends on memory, which could lead to bias • Clinical data may be inadequate or incomplete • “Case group” may not be homogenous - criteria for diagnosis may differ. • Clinical cases are selective survivors

  18. Cons • Non-representativeness of cases. Those coming in for treatment may differ from those not seeking treatment and those going somewhere else. • Antecedent is not obtained from universe of all antecedents. • Berkson’s fallacy - making generalizations from hospital or clinical samples to the general population. • Cannot know what association would be for all or for a representative sample of all people having the antecedent.

  19. References • For Internet Resources on the topics covered in this lecture, check out my Web site: • http://www.bettycjung.net/

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