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The cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of "no treatment effect" are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a "successful" trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared "unsuccessful" due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a "prior" for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making. [ABSTRACT FROM AUTHOR]

The cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of "no treatment effect" are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a "successful" trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared "unsuccessful" due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a "prior" for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making. [ABSTRACT FROM AUTHOR]

The marker-stratified design (MSD) is an important design to assess treatment and marker effects in personalized medicine. The MSD stratifies patients into marker positive and marker negative subgroups on the basis of their biomarker profiles and then randomizes them to the standard treatment or a new treatment within each subgroup. The performance of the MSD can be seriously undermined when the biomarker is measured with error (or misclassified). A recently proposed analytic method corrects the biomarker misclassification in the MSD under the assumptions that the biomarker classification rates are known and no other covariates need to be adjusted. We propose a two-stage MSD to relax these assumptions. We analytically investigate the bias in the estimation of prognostic and predictive marker effects and treatment effects caused by biomarker misclassification in the presence of covariates, and we propose an expectation-maximization algorithm to correct such biases. The design does not require prespecification of the misclassification rates and can incorporate any covariates that potentially confound the prognostic and predictive marker effects and treatment effect. Numerical trial applications show that the method has desirable operating characteristics. [ABSTRACT FROM AUTHOR]