MethodsUse of Time-Dependent Propensity Scores to Adjust Hazard Ratio Estimates in Cohort Studies with Differential Depletion of SusceptiblesWyss, Richarda; Gagne, Joshua J.a; Zhao, Yueqinb; Zhou, Esther H.b; Major, Jacqueline M.b; Wang, Shirley V.a; Desai, Rishi J.a; Franklin, Jessica M.a; Schneeweiss, Sebastiana; Toh, Sengweec; Johnson, Margaretc; Fireman, BrucedAuthor Information From the aDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA bCenter for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD cDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA dDivision of Research, Kaiser Permanente Northern California, Oakland, CA. Submitted January 23, 2019; accepted September 23, 2019. This project was funded by the FDA through Sentinel contract number HHSF223200910006I. The authors report no conflicts of interest. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). Software for the methods discussed in the manuscript is available at https://github.com/richiewyss/tdps-conditionalHR. Correspondence: Richard Wyss, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital and Harvard Medical School, 1620 Tremont St. Suite 3030, Boston, MA 02120. E-mail: firstname.lastname@example.org. Epidemiology: January 2020 - Volume 31 - Issue 1 - p 82-89 doi: 10.1097/EDE.0000000000001107 Buy SDC Metrics Abstract Estimating hazard ratios (HR) presents challenges for propensity score (PS)-based analyses of cohorts with differential depletion of susceptibles. When the treatment effect is not null, cohorts that were balanced at baseline tend to become unbalanced on baseline characteristics over time as “susceptible” individuals drop out of the population at risk differentially across treatment groups due to having outcome events. This imbalance in baseline covariates causes marginal (population-averaged) HRs to diverge from conditional (covariate-adjusted) HRs over time and systematically move toward the null. Methods that condition on a baseline PS yield HR estimates that fall between the marginal and conditional HRs when these diverge. Unconditional methods that match on the PS or weight by a function of the PS can estimate the marginal HR consistently but are prone to misinterpretation when the marginal HR diverges toward the null. Here, we present results from a series of simulations to help analysts gain insight on these issues. We propose a novel approach that uses time-dependent PSs to consistently estimate conditional HRs, regardless of whether susceptibles have been depleted differentially. Simulations show that adjustment for time-dependent PSs can adjust for covariate imbalances over time that are caused by depletion of susceptibles. Updating the PS is unnecessary when outcome incidence is so low that depletion of susceptibles is negligible. But if incidence is high, and covariates and treatment affect risk, then covariate imbalances arise as susceptibles are depleted, and PS-based methods can consistently estimate the conditional HR only if the PS is periodically updated. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.