Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology
Autor: | Frank Bretz, Paul Gallo, Michael Branson, Martin Posch, Emmanuel Zuber, Amy Racine-Poon, Werner Brannath |
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Rok vydání: | 2009 |
Předmět: |
Statistics and Probability
Biometry Epidemiology Computer science Bayesian probability Population Posterior probability computer.software_genre Decision Support Techniques Clinical Trials Phase II as Topic Neoplasms Humans education Selection (genetic algorithm) Clinical Trials as Topic Likelihood Functions education.field_of_study Models Statistical Patient Selection Bayes Theorem Interim analysis Confidence interval Clinical Trials Phase III as Topic Data mining Computerized adaptive testing computer Type I and type II errors |
Zdroj: | Statistics in Medicine. 28:1445-1463 |
ISSN: | 1097-0258 0277-6715 |
DOI: | 10.1002/sim.3559 |
Popis: | The ability to select a sensitive patient population may be crucial for the development of a targeted therapy. Identifying such a population with an acceptable level of confidence may lead to an inflation in development time and cost. We present an approach that allows to decrease these costs and to increase the reliability of the population selection. It is based on an actual adaptive phase II/III design and uses Bayesian decision tools to select the population of interest at an interim analysis. The primary endpoint is assumed to be the time to some event like e.g. progression. It is shown that the use of appropriately stratified logrank tests in the adaptive test procedure guarantees overall type I error control also when using information on patients that are censored at the adaptive interim analysis. The use of Bayesian decision tools for the population selection decision making is discussed. Simulations are presented to illustrate the operating characteristics of the study design relative to a more traditional development approach. Estimation of treatment effects is considered as well. |
Databáze: | OpenAIRE |
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