Bayesian enrichment strategies for randomized discontinuation trials

48 mins 35 secs, 202.42 MB, Flash Video 484x272, 29.97 fps, 44100 Hz, 568.86 kbits/sec

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Description:	Rosner, G (Johns Hopkins) Wednesday 10 August 2011, 14:00-14:45

Created:

2011-08-11 15:34

Collection:

Design and Analysis of Experiments

Publisher:

Isaac Newton Institute

Rosner, G

Language:

eng (English)

Credits:

Author:	Rosner, G
Director:	Steve Greenham


Abstract:	We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identi?es a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identi?ed subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We de?ne a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.

Abstract:

We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identi?es a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identi?ed subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We de?ne a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.

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