Simulation optimization for Bayesian multi-arm multi-stage clinical trial with binary endpoints
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TL;DR: This paper introduces a generic process for simulating Bayesian multi-arm multi-stage designs with binary endpoints and optimize the method for calculating the posterior probability and posterior predictive probability of success.
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Abstract: Multi-arm multi-stage designs, in which multiple active treatments are compared to a control and accumulated information from interim data are used to add or remove arms from the trial, may reduce development costs and shorten the drug development timeline. As such, this adaptive update is a natural complement to Bayesian methodology in which the prior clinical belief is sequentially updated using the observed probability of success. Simulation is often required for planning clinical trials to accommodate the complexity of the design and to optimize key design characteristics. This paper addresses two key limiting factors in simulations, namely the computational burden and the time needed to obtain results. We first introduce a generic process for simulating Bayesian multi-arm multi-stage designs with binary endpoints. Then, to address the computational burden and time, we optimize the method for calculating the posterior probability and posterior predictive probability of success.
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References
Discrete sequential boundaries for clinical trials
K. K. Gordon Lan,David L. DeMets +1 more
TL;DR: In this article, the authors proposed a more flexible method to construct discrete sequential boundaries based on the choice of a function, a*(t), which characterizes the rate at which the error level ac is spent.
2K
Bayesian Approaches to Randomized Trials
TL;DR: It is argued that a Bayesian approach allows a formal basis for using external evidence and in addition provides a rational way for dealing with issues such as the ethics of randomization, trials to show treatment equivalence, the monitoring of accumulating data and the prediction of the consequences of continuing a study.
663
Current Status of Neuroprotection for Cerebral Ischemia Synoptic Overview
TL;DR: Of the approximately 160 clinical trials of neuroprotection for ischemic stroke conducted as of late 2007, only approximately 40 represent larger-phase completed trials, and fully one half of the latter utilized a window to treatment of >6 hours, despite strong preclinical evidence that this delay exceeds the likely therapeutic window of efficacy in acute stroke.
254
Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls
TL;DR: A historical overview of the early days, review the key methodological concepts and summarize regulatory and industry perspectives on such designs are provided, and the application of adaptive designs is illustrated with three case studies.
The utility of Bayesian predictive probabilities for interim monitoring of clinical trials.
TL;DR: In this paper, the authors explore settings in which Bayesian predictive probabilities are advantageous for interim monitoring compared to Bayesian posterior probabilities, p-values, conditional power, or group sequential methods.
161