The article discusses BayesPPDSurv, a statistical software designed to enhance clinical trial designs by integrating historical datasets through power prior models. It introduces two model fitting functions and sample size determination methodologies, enabling researchers to generate posterior samples and estimate Bayesian power. A case study featuring melanoma clinical trials illustrates the application of BayesPPDSurv, demonstrating its practical utility in refining trial designs through advanced Bayesian methods. By using fixed or random power priors, the software improves accuracy and efficiency in statistical analysis for survival data related to clinical research.
BayesPPDSurv allows the integration of historical data using both fixed and random power prior models, enhancing design efficacy in clinical trials involving survival data.
The software provides functions for fitting models and determining sample sizes, efficiently returning crucial statistical estimates to inform trial design decisions.
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