Balancing Automated Procedures for Confounding Control with Background Knowledge

In the publication by Patorno et al.1 found in this issue of Epidemiology, the authors illustrate the importance of using subject matter knowledge to complement the automated high-dimensional propensity score (hdPS) algorithm when controlling for confounding in studies based on claims data with few exposed outcomes. The topic of variable selection for PS models in settings involving large numbers of potential confounders has received considerable attention in recent years. This interest is in part due to the uncertainty in determining what role automated procedures should play in the variable selection process. With large healthcare databases becoming increasingly used in epidemiology,2–4 automated procedures can be beneficial in selecting potential confounders that are unknown to the investigator.5–7 Further, the application of automated procedures is likely to expand as safety surveillance receives more attention as part of the Food and Drug Administration’s Sentinel Initiative.8 In these settings, automated procedures, such as the hdPS, can increase the speed and efficiency of active surveillance.7 With an increasing need for automated methods for confounding control in these areas of epidemiologic research, the question becomes: how should investigators balance automated procedures with the use of subject matter knowledge?

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