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.x | DOI Listing |
Biometrics
March 2012
Center for Innovation in Clinical Research & Drug Development, ClinResearch GmbH, Köln, Germany.
Biometrics
March 2012
Statistical Methodology, Development, Novartis Pharma AG, Basel, Switzerland.
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 PDFBiometrics
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 PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!