Discussion of the article by Trippa, Rosner, and Müller on Bayesian enrichment strategies for randomized discontinuation trials.

Biometrics

Statistical Methodology, Development, Novartis Pharma AG, Basel, Switzerland.

Published: March 2012

We congratulate Trippa, Rosner, and Müller (2011, Biometrics, in press) on an intriguing and timely article. The randomized discontinuation design (RDD) has only recently been used in cancer clinical trials, and methodological understanding on how to best design such studies is limited. The authors’ approach to optimize RDD designs based on prior information and utility considerations is an important step forward. A noteworthy element is their use of a semimechanistic model to describe tumor growth. Mathematical models have provided considerable insight on the complex process of tumor evolution (Preziosi, 2003, Cancer Modelling and Simulation). Utilizing this knowledge should lead to better design, analysis, and decisions.

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http://dx.doi.org/10.1111/j.1541-0420.2011.01624.xDOI Listing

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We congratulate Trippa, Rosner, and Müller (2011, Biometrics, in press) on an intriguing and timely article. The randomized discontinuation design (RDD) has only recently been used in cancer clinical trials, and methodological understanding on how to best design such studies is limited. The authors’ approach to optimize RDD designs based on prior information and utility considerations is an important step forward.

View Article and Find Full Text PDF

Bayesian enrichment strategies for randomized discontinuation trials.

Biometrics

March 2012

Harvard School of Public Health and Department of Biostatistics, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.

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.

View Article and Find Full Text PDF

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