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Abstract:
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This paper outlines a general framework in which clinical trial simulations are employed integrating both pharmacometric and statistical analyses to support trial design and quantitative decision making in drug development. Specifically, predictive pharmacometric models are used as data-generation models to simulate data, while data-analytic models as specified in the statistical analysis plan are used to analyze the simulated data, and to apply a quantitative data-analytic decision rule. Various probability metrics including probability of achieving the target value, probability of success, and probability of a correct decision are proposed to support study design recommendations and quantitative decision-making. A case study is presented to illustrate the clinical trial simulation methods and procedures.
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