The threat of bias amplification extends to all epidemiologic methods that rely on conditioning on covariates, so understanding its mechanisms and implications is important. We thank Dr. Pearl (1) for initially bringing the issue to our attention and for now extending the analysis of bias amplification to the case of multiple covariates (2). Here, as in our original paper (3), we attempt to clarify the implications of these results in practical settings. In particular, we focus on secondary analysis of electronically collected data sets, where there may be hundreds or thousands of measured variables available for use in adjustment. Many of these covariates may be proxies of true confounders, even if their relations with outcome and exposure are uncertain.
