Two-stage treatment strategies based on sequential failure times
Seminar Room 1, Newton Institute
AbstractFor many diseases, therapy involves multiple stages, with treatment in each stage chosen adaptively based on the patient's current disease status and history of previous treatments and outcomes. Physicians routinely use such multi-stage treatment strategies, also called dynamic treatment regimes or treatment policies. In this talk, I will present a Bayesian framework for a clinical trial comparing several two-stage strategies based on the time to overall failure, defined as either second disease worsening or discontinuation of therapy. The design was motivated by a clinical trial, which is currently ongoing, comparing six two-stage strategies for treating advanced kidney cancer. Each patient is randomized among a set of treatments at enrollment, and if disease worsening occurs the patient is then re-randomized among a set of treatments excluding the treatment received initially. The goal is to select the two-stage strategy giving the largest mean overall failure time. A parametric model is formulated to account for non-constant failure time hazards, regression of the second failure time on the patient's first worsening time, and the complications that the failure time in either stage may be interval censored and there may be a delay between first and second stage of therapy. A simulation study in the context of the kidney cancer trial is presented.
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