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Lifetime Data Anal (2012) 18:446469 DOI 10.1007/s10985-012-9224-6
Robust inference in discrete hazard models for randomized clinical trials
Vinh Q. Nguyen Daniel L. Gillen
Received: 24 October 2011 / Accepted: 29 June 2012 / Published online: 19 July 2012 Springer Science+Business Media, LLC 2012
Abstract Time-to-event 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 discrete-time 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 nite samples.
Keywords Censoring Estimating equations Discrete survival endpoints
Model misspecication Robust inference
1 Introduction
It is often the case in survival studies that the event of interest is not observed the instant it occurs due to a lack of obvious signals indicating event occurrence. In such settings, the realization of the event is usually only detectable by a complicated and costly screening test. For example, cancer diagnosis or disease progression are often
V. Q. Nguyen (B) D. L. Gillen
Department of Statistics, University of California, Irvine, CA, USA e-mail: [email protected]
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not visibly identiable and can only be detected through a screening procedure such as a CT scan, mammography, colonoscopy, or blood test. Repeated screenings at ne time intervals (e.g., daily) to determine the exact event time are impractical due to limited resources and the adverse impact on quality of life for the patients. When it is of interest to monitor such events, a more...