1. System error Biases in epidemiological studies FETP India

2. Competency to be gained from this lecture Prevent biases that may be avoided and understand the effect of others

3. Key elements Definition of biases Selection biases Information biases Controlling the effect of biases

4. Misclassification Non-differential misclassification  Error on outcome status independent from exposure status  Error on exposure status independent from outcome status Differential misclassification (bias)  Error influenced by outcome or exposure status Biases

5. Effect of misclassification Non-differential misclassification  Usually reduces the strength of association between outcome and exposure (RR or OR closer to 1)  Is not a sufficient reason to dismiss the results of a study reporting an association Differential misclassification (bias)  May commonly increase the strength of the association  May also reduce the strength of the association Biases

6. Definition of a bias Systematic error in the protocol of a study that leads to a distortion of measurement affecting internal validity How to understand the term systematic? Error systematically done in the same way  Differential misclassification Error of the system  Built in misconception of the protocol Biases

7. Types of biases Selection biases  Biases in the way subjects enter a study Information biases  Biases in the way information is collected after inclusion in a study Biases

8. Level of operation of biases Selection biases Information biases Biases

9. Selection biases Type of study Inclusion into the study Sources of selection biases Case control On the basis of outcome Selection of cases or controls with specific exposure status Cohort On the basis of exposure Selection of exposed or unexposed with specific risk Selection biases

10. Examples of selection biases in a cohort study Subjects selected on the basis of exposure Outcome not independently distributed among exposed and unexposed  Systematic selection of exposed subjects with: Higher risk of outcome Lower risk of outcome  Systematic selection of unexposed subjects with: Higher risk of outcome Lower risk of outcome Ab cd aB cd ab cD ab Cd Selection biases

11. Examples of selection biases in a case control study Subjects selected on the basis of outcome Exposure not independently distributed among cases and controls  Systematic selection of case-patients with: Higher frequency of exposure Lower frequency of exposure  Systematic selection of control-subjects with: Higher frequency of exposure Lower frequency of exposure Ab cd ab Cd ab cD aB cd Selection biases

12. Sources of selection biases Surveillance Screening / diagnosis Admission to health care facilities Selective survival Non-response / loss to follow up Selection biases

13. Sources of selection biases Surveillance  Systematic notification of cases exposed Screening / diagnosis Admission to health care facilities Selective survival Non-response / loss to follow up Selection biases

14. Sources of selection biases Surveillance Screening / diagnosis  Systematic case search among exposed Admission to health care facilities Selective survival Non-response / loss to follow up Selection biases

15. Sources of selection biases Surveillance Screening / diagnosis Admission to health care facilities  Systematic admission of: Case-patients exposed / unexposed Control-subjects exposed / unexposed Selective survival Non-response / loss to follow up Selection biases

16. Sources of selection biases Surveillance Screening / diagnosis Admission to health care facilities Selective survival  Systematic inclusion of cases who survived and who may be more or less exposed Non-response / loss to follow up Selection biases

17. Sources of selection biases Surveillance Screening / diagnosis Admission to health care facilities Selective survival Non-response / loss to follow up  Systematic inclusion of subjects more likely to participate who may be: More or less exposed More or less at risk Selection biases

18. Information biases Type of study Collection of information Sources of information biases Case control About exposure Collection of information leaning towards specific exposure status Cohort About outcome Collection of information leaning towards specific outcome status Information biases

19. Examples of information biases in a cohort study Subjects selected on the basis of exposure Outcome not independently measured among exposed and unexposed  Measurement of a higher incidence of outcome: Among exposed Among unexposed  Measurement of a lower incidence of outcome: Among exposed Among unexposed Ab cd ab Cd ab cD aB cd Information biases

20. Examples of information biases in a case control study Subjects selected on the basis of outcome Exposure not independently measured among cases and controls :  Measurement of a higher frequency of exposure: Among cases Among controls  Measurement of a lower frequency of exposure: Among cases Among controls Ab cd aB cd ab cD ab Cd Information biases

21. Sources of information biases Recall Investigator Data quality Prevarication Information biases

22. Sources of information biases Recall  Cases may recall exposure more than controls Investigator Data quality Prevarication Information biases

23. Sources of information biases Recall Investigator  Systematic collection of information supporting expected conclusions Unconsciously Consciously  May be examined in the analysis Data quality Prevarication Information biases

24. Sources of information biases Recall Investigator Data quality  Better exposure data available on cases  Better outcome data available on exposed Prevarication Information biases

25. Sources of information biases Recall Investigator Data quality Prevarication  Systematic distortion of the truth by subjects Information biases

26. Other limitations also referred to as biases Intervention biases  Non-specific effect of interventions in a trial Analysis biases  Non-systematic statistical analyses Interpretation biases  Investigators with pre-conceived ideas Publication biases  Negative studies do not get published

27. Checklist to prevent biases when preparing a protocol (1/2) 1.Is the sample representative?  Are controls representative of the population from which cases are drawn? 2.Are the exposure and outcome criteria:  Standardized?  Specific?  Clear?  Well measured?  Applied consistently by trained field workers? Controlling biases

28. Checklist to prevent biases when preparing a protocol (2/2) 3.Could exposure status affect the probability to detect the outcome? 4.Could outcome status affect the probability to recall the exposure? 5.What will be the proportion of response? 6.What will be the proportion of “lost to follow up”? Controlling biases

29. Discussing biases critically Review critically selection and information biases that are likely to have operated Review the potential effect of each of these biases (e.g., under- or overestimation) Review what can be done to address these biases a posteriori (e.g., analysis plan) Analyze possible summary effect of all these biases on the final results Interpret data in light of this summary effect Controlling biases

30. Take-home messages Have a strong design that prevents biases Examine how subjects enter a study Collect information in a standardized way Prevent the biases you can avoid, understand those you cannot avoid

31. How differential misclassification leads to biases Biases