Understanding lack of validity: Bias

1. Understanding lack of validity: Bias

2. Objectives • To define and discuss the concept of bias • To define and discuss selection bias and information bias. • To discuss exposure and outcome identification bias • To discuss the results of information bias • Combine selection/information bias: detection bias, incidence-prevalence bias, temporal bias

3. Bias - Definition • Is defined as the result of a systematic error in the design or conduct of a study • Results from: • Flaws in the method of selection of study participants • Procedures for gathering relevant exposure and/or disease information

5. Bias - definition • Bias exists when, on the average, the results of an infinite number of studies differs from the true results • Most biases related to study design and procedures can be classified into: • Selection bias • Information bias

8. Information bias • A systematic tendency for individuals selected for inclusion in the study to be erroneously placed in different exposure/outcome categories (misclassification) • Examples: recall bias – ability to recall past exposure is dependent on case-control status.

9. Selection bias • Systematic error in the ascertainment of study subjects • Berksonian bias – when this bias occurs in case-control studies of hospitalized patients.

10. Recall bias • When recall of past exposure error differs between cases and controls • Methods to prevent recall bias • Review of other documentation (eg. pharmacy or hospital charts) • Using proxy respondents (spouse, parent, etc) • Using biological markers

11. Interviewer bias • When the disease status is not masked and the interviewer differentially ascertains exposure status • Methods to reduce this error: sub-study interviews, masking of case-control status, standardization in how you ask the question

12. Outcome identification bias • May occur in both case-control and cohort • Usually due to an imperfect definition of the outcome or errors in the data collection stage. • Observer bias • Respondent bias

13. Results of information bias • Non-differential misclassificaton (systematic) • Differential (non-systematic)

14. Effect of misclassification of a confounding variable • Results in an imperfect adjustment due to residual confounding • Can lead to spurious conclusions

15. The association of IV drug use with CD4 cell counts

17. Medical surveillance bias • Can be selection or information bias • This bias is most likely to occur when the exposure is a medical condition or therapy that leads to frequent/detailed checkups (eg:diabetes, OCP)

18. Cross-sectional bias • Incidence-prevalence bias – results from the inclusion of prevalent cases into the study (important when duration of disease is differential) • Temporal bias – don’t know which came first exposure or disease

20. Lead time bias • The time by which a diagnosis can be advanced by screening • Occurs when estimating survival time

22. Publication bias • Assumption that published papers should be unbiased and represent an unbiased sample of the theoretical “population” of unbiased studies • Papers with statistically significant results are more likely to be published