Abstract
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.
Original language | English (US) |
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Pages (from-to) | 2802-2815 |
Number of pages | 14 |
Journal | Statistics in Medicine |
Volume | 27 |
Issue number | 15 |
DOIs | |
State | Published - Jul 10 2008 |
Keywords
- Adaptive design
- Bayesian design
- Futility rule
- Phase II clinical trial
- Simulation
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
MD Anderson CCSG core facilities
- Clinical Trials Office