Ming-Dauh Wang

Publications (2)3.13 Total impact

• Article: Sample Size Reestimation by Bayesian Prediction.

  Ming-Dauh Wang
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  ABSTRACT: We review a Bayesian predictive approach for interim data monitoring and propose its application to interim sample size reestimation for clinical trials. Based on interim data, this approach predicts how the sample size of a clinical trial needs to be adjusted so as to claim a success at the conclusion of the trial with an expected probability. The method is compared with predictive power and conditional power approaches using clinical trial data. Advantages of this approach over the others are discussed. ((c) 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
  Biometrical Journal 10/2006; · 1.25 Impact Factor
• Article: Bayesian predictive approach to interim monitoring in clinical trials.

  Alexei Dmitrienko, Ming-Dauh Wang
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  ABSTRACT: This paper reviews Bayesian strategies for monitoring clinical trial data. It focuses on a Bayesian stochastic curtailment method based on the predictive probability of observing a clinically significant outcome at the scheduled end of the study given the observed data. The proposed method is applied to derive efficacy and futility stopping rules in clinical trials with continuous, normally distributed and binary endpoints. The sensitivity of the resulting stopping rules to the choice of prior distributions is examined and guidelines for choosing a prior distribution of the treatment effect are discussed. The Bayesian predictive approach is compared to the frequentist (conditional power) and mixed Bayesian-frequentist (predictive power) approaches. The interim monitoring strategies discussed in the paper are illustrated using examples from a small proof-of-concept study and a large mortality trial.
  Statistics in Medicine 08/2006; 25(13):2178-95. · 1.88 Impact Factor