Principles of Epidemiology CORE 5520 (32832 - 匹.ppt

Bias, Confounding and the Role of Chance Principles of Epidemiology Lecture 5 Dona Schneider, PhD, MPH, FACE To Show Cause We Use Koch’s Postulates for Infectious Disease Hill’s Postulates for Chronic Disease and Complex Questions Strength of Association – Tonight’s entire lecture Biologic Credibility Specificity Consistency with Other Associations Time Sequence Dose-Response Relationship Analogy Experiment Coherence To Show a Valid Statistical Association We need to assess: Bias: whether systematic error has been built into the study design Confounding: whether an extraneous factor is related to both the disease and the exposure Role of chance: how likely is it that what we found is a true finding BIAS Systematic error built into the study design Selection Bias Information Bias Types of Selection Bias Berksonian bias – There may be a spurious association between diseases or between a characteristic and a disease because of the different probabilities of admission to a hospital for those with the disease, without the disease and with the characteristic of interest Berkson J. Limitations of the application of fourfold table analysis to hospital data. Biometrics 1946;2:47-53 Types of Selection Bias (cont.) Response Bias – those who agree to be in a study may be in some way different from those who refuse to participate Volunteers may be different from those who are enlisted Types of Information Bias Interviewer Bias – an interviewer’s knowledge may influence the structure of questions and the manner of presentation, which may influence responses Recall Bias – those with a particular outcome or exposure may remember events more clearly or amplify their recollections Types of Information Bias (cont.) Observer Bias – observers may have preconceived expectations of what they should find in an examination Loss to follow-up – those that are lost to follow-up or who withdraw from the study may be different from those who are followed for the entire study Information Bi