Bias in claims data with respect to disease status is a problem for health services researchers, because we often rely on administrative claims (billing data) to measure disease status for large cohorts. Misclassification bias may alter the prevalence of given conditions–which is especially problematic for epidemiology and comparative effectiveness research. It may even alter the… Read More »
Comparative Effectiveness Research (CER) seeks to compare alternative treatments and ways to deliver healthcare to inform healthcare decisions. It can provide evidence of the harms, benefits, and effectiveness of different treatment options. As the number of studies in CER continues to grow, it is vitally important that the types of bias that exist as a function of the study design be explained. In a Medical Care article published in April, Dr. Sebastien Haneuse lays out definitions and examples of selection bias and confounding bias in CER, with a particular emphasis on distinguishing between the two.