ArticleTranslational cancer research2019
Application of Bayesian predictive probability for interim futility analysis in single-arm phase II trial.
Article in Translational cancer research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Does prefusion F protein-based respiratory syncytial virus immunization in pregnancy safely promote transplacental transfer of neutralizing antibodies?Journal of perinatology : official journal of the California Perinatal Association · 2024Article
- Applications of the prediction of satisfaction design for monitoring single-arm phase II trials.PloS one · 2024Article
- Latent process model of the 6-minute walk test in Duchenne muscular dystrophy : A Bayesian approach to quantifying rare disease progression.Journal of pharmacokinetics and pharmacodynamics · 2020Article
- Clinical Value of SARS-CoV2 IgM and IgG Antibodies in Diagnosis of COVID-19 in Suspected Cases.Journal of inflammation research · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundBayesian predictive probability design, with a binary endpoint, is gaining attention for the phase II trial due to its innovative strategy. To make the Bayesian design more accessible, we elucidate this Bayesian approach with a R package to streamline a statistical plan, so biostatisticians and clinicians can easily integrate the design into clinical trial.
methodsWe utilize a Bayesian framework using Bayesian posterior probability and predictive probability to build a R package and develop a statistical plan for the trial design. With pre-defined sample sizes, the approach employs the posterior probability with a threshold to calculate the minimum number of responders needed at end of the study to claim efficacy. Then the predictive probability is applied to evaluate future success at interim stages and form stopping rule at each stage.
resultsAn R package, 'BayesianPredictiveFutility', with associated graphical interface is developed for easy utilization of the trial design. The statistical tool generates a professional statistical plan with comprehensive results including a summary, details of study design, a series of tables and figures from stopping boundary for futility, Bayesian predictive probability, performance [probability of early termination (PET), type I error, and power], PET at each interim analysis, sensitivity analysis for predictive probability, posterior probability, sample size, and beta prior distribution. The statistical plan presents the methodology in a readable language fashion while preserving rigorous statistical arguments. The output formats (Word or PDF) are available to communicate with physicians or to be incorporated in the trial protocol. Two clinical trials in lung cancer are used to demonstrate its usefulness.
conclusionsBayesian predictive probability method presents a flexible design in clinical trial. The statistical tool brings an added value to broaden the application.
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