title: A Bayesian dose-finding design for phase I/II clinical trials with nonignorable dropouts.
abstract: Phase I/II trials utilize both toxicity and efficacy data to achieve efficient dose finding. However, due to the requirement of assessing efficacy outcome, which often takes a long period of time to be evaluated, the duration of phase I/II trials is often longer than that of the conventional dose-finding trials. As a result, phase I/II trials are susceptible to the missing data problem caused by patient dropout, and the missing efficacy outcomes are often nonignorable in the sense that patients who do not experience treatment efficacy are more likely to drop out of the trial. We propose a Bayesian phase I/II trial design to accommodate nonignorable dropouts. We treat toxicity as a binary outcome and efficacy as a time-to-event outcome. We model the marginal distribution of toxicity using a logistic regression and jointly model the times to efficacy and dropout using proportional hazard models to adjust for nonignorable dropouts. The correlation between times to efficacy and dropout is modeled using a shared frailty. We propose a two-stage dose-finding algorithm to adaptively assign patients to desirable doses. Simulation studies show that the proposed design has desirable operating characteristics. Our design selects the target dose with a high probability and assigns most patients to the target dose.
authors: Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA, U.S.A.
authors: Beibei
authors: B
authors: Guo
authors: Ying
authors: Y
authors: Yuan
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D017321:Clinical Trials, Phase I as Topic / Q000379:methods* / Q000706:statistics & numerical data; D017322:Clinical Trials, Phase II as Topic / Q000379:methods* / Q000706:statistics & numerical data; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D064420:Drug-Related Side Effects and Adverse Reactions; D023381:Endpoint Determination; D006801:Humans; D016015:Logistic Models; D020714:Maximum Tolerated Dose; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D016016:Proportional Hazards Models; D012107:Research Design; D013997:Time Factors; D016896:Treatment Outcome
keywords: adaptive design; dose finding; dropout; nonignorable missing data; phase I/II trial
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