Robust inference in discrete hazard models for randomized clinical trials
 Vinh Q. Nguyen,
 Daniel L. Gillen
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Timetoevent data in which failures are only assessed at discrete time points are common in many clinical trials. Examples include oncology studies where events are observed through periodic screenings such as radiographic scans. When the survival endpoint is acknowledged to be discrete, common methods for the analysis of observed failure times include the discrete hazard models (e.g., the discretetime proportional hazards and the continuation ratio model) and the proportional odds model. In this manuscript, we consider estimation of a marginal treatment effect in discrete hazard models where the constant treatment effect assumption is violated. We demonstrate that the estimator resulting from these discrete hazard models is consistent for a parameter that depends on the underlying censoring distribution. An estimator that removes the dependence on the censoring mechanism is proposed and its asymptotic distribution is derived. Basing inference on the proposed estimator allows for statistical inference that is scientifically meaningful and reproducible. Simulation is used to assess the performance of the presented methodology in finite samples.
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 Title
 Robust inference in discrete hazard models for randomized clinical trials
 Journal

Lifetime Data Analysis
Volume 18, Issue 4 , pp 446469
 Cover Date
 20121001
 DOI
 10.1007/s1098501292246
 Print ISSN
 13807870
 Online ISSN
 15729249
 Publisher
 Springer US
 Additional Links
 Topics
 Keywords

 Censoring
 Estimating equations
 Discrete survival endpoints
 Model misspecification
 Robust inference
 Industry Sectors
 Authors

 Vinh Q. Nguyen ^{(1)}
 Daniel L. Gillen ^{(1)}
 Author Affiliations

 1. Department of Statistics, University of California, Irvine, CA, USA