Levintow SN, Nielson CM, Hernandez RK, Breskin A, Pritchard D, Lash TL, Rothman KJ, Gilbertson D, Muntner P, Critchlow C, Brookhart MA, Bradbury BD. Pragmatic considerations for negative control outcome studies to guide non-randomized comparative analyses: a narrative review. Pharmacoepidemiol Drug Saf. 2023 Jun;32(6):599-606. doi: 10.1002/pds.5623

This narrative review describes the application of negative control outcome (NCO) methods to assess potential bias due to unmeasured or mismeasured confounders in non-randomized comparisons of drug effectiveness and safety. An NCO is assumed to have no causal relationship with a treatment under study while subject to the same confounding structure as the treatment and outcome of interest; an association between treatment and NCO then reflects the potential for uncontrolled confounding between treatment and outcome. We focus on two recently completed NCO studies that assessed the comparability of outcome risk for patients initiating different osteoporosis medications and lipid-lowering therapies, illustrating several ways in which confounding may result. In these studies, NCO methods were implemented in claims-based data sources, with the results used to guide the decision to proceed with comparative effectiveness or safety analyses. Based on this research, we provide recommendations for future NCO studies, including considerations for the identification of confounding mechanisms in the target patient population, the selection of NCOs expected to satisfy required assumptions, the interpretation of NCO effect estimates, and the mitigation of uncontrolled confounding detected in NCO analyses. We propose the use of NCO studies prior to initiating comparative effectiveness or safety research, providing information on the potential presence of uncontrolled confounding in those comparative analyses. Given the increasing use of non-randomized designs for regulatory decision-making, the application of NCO methods will strengthen study design, analysis, and interpretation of real-world data and the credibility of the resulting real-world evidence.

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