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Accounting for patient heterogeneity in phase II clinical trials
Author(s) -
Wathen J. Kyle,
Thall Peter F.,
Cook John D.,
Estey Elihu H.
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3109
Subject(s) - prior probability , clinical trial , bayesian probability , subgroup analysis , hyperparameter , computer science , event (particle physics) , bayes' theorem , statistics , medicine , econometrics , mathematics , artificial intelligence , confidence interval , physics , quantum mechanics
Phase II clinical trials typically are single‐arm studies conducted to decide whether an experimental treatment is sufficiently promising, relative to standard treatment, to warrant further investigation. Many methods exist for conducting phase II trials under the assumption that patients are homogeneous. In the presence of patient heterogeneity, however, these designs are likely to draw incorrect conclusions. We propose a class of model‐based Bayesian designs for single‐arm phase II trials with a binary or time‐to‐event outcome and two or more prognostic subgroups. The designs' early stopping rules are subgroup specific and allow the possibility of terminating some subgroups while continuing others, thus providing superior results when compared with designs that ignore treatment–subgroup interactions. Because our formulation requires informative priors on standard treatment parameters and subgroup main effects, and non‐informative priors on experimental treatment parameters and treatment–subgroup interactions, we provide an algorithm for computing prior hyperparameter values. A simulation study is presented and the method is illustrated by a chemotherapy trial in acute leukemia. Copyright © 2007 John Wiley & Sons, Ltd.