Potential Errors In Epidemiologic Studies

1. Potential Errors In Epidemiologic Studies Confounding IV. Dr. Sherine Shawky

2. Learning Objectives • Understand the concept of confounding • Recognize the methods to prevent confounding • Know the methods to evaluate the impact of confounding

3. Performance Objectives • Prevent confounding • Evaluate confounding

4. Confounding A situation in which effects of two risk factors are mixed in the occurrence of the health problem under study

5. Confounding may lead to overestimation or under-estimation of the true association between exposure and outcome and can even change the direction of the observed effect

6. Criteria for Confounding Exposure Outcome Confounding

7. Control of Confounding Prevent Study Evaluate

8. Prevention of Confounding Matching Restriction Randomization

9. Restriction Putting admissibility criteria for subjects and limiting enrollment into the study to individuals who fall within a specified category or categories of the confounder.

10. Restriction Strength • Straightforward • Convenient if criteria are narrow • Inexpensive

11. RestrictionLimitation • Reduces the number of subjects eligible to participate • Difficult if criteria are not narrow • Does not permit evaluation of association between exposure and outcome for varying levels of factor

12. Randomization Every individual has the same chance of being classified in either of the two groups. The two methods commonly used for randomization are the use of a table of random numbers or the use of a computer-generated randomization

13. Randomization Strength • Controls confounders even those unsuspected • Study groups are comparable • Permits evaluation of association between exposure and outcome for varying levels of the factor

14. Randomization Limitation • Not easy to perform • Ethical problems • Expensive

15. Matching Selecting study group and comparison group so that they are comparable with respect to various defined factors. In addition to the use of specific statistical tests for analyses of paired data

16. MatchingStrength • Appropriate when sample size is small and matching variables are limited • Study groups are comparable

17. Matching Limitation • Difficult, expensive and time consuming to find comparison subjects • Matching on a particular variable prohibits studying its association with the outcome

18. Evaluation of Confounding Stratified analysis Multivariate analysis

19. Stratified Analysis Stratification is a technique used to control confounding in the analysis stage that involves the evaluation of the association within homogeneous categories or strata of the confounding factor

20. Stratification Strength • Easy for limited variables with limited number of categories • Permits evaluation of confounding and interaction • Permits evaluation of association between exposure and outcome for varying levels of the factor

21. Stratification Limitation Difficult if many variables with varying number of categories are required

22. Multivariate Analysis Analysis of data through construction of mathematical model that takes into account number of variables at the same time

23. Multivariate AnalysisStrength Describes efficiently the association between exposure and outcome taking in consideration the impact of other variables.

24. Multivariate AnalysisLimitation The choice of the appropriate model is complex and requires training and experience

25. Conclusion There are number of methods for control of confounding. Each has its strength and limitation. In most situations, a combination of strategies provides better insights of data and more efficient control of confounding.