Bayesian design of a survival trial with a cured fraction using historical data.

TitleBayesian design of a survival trial with a cured fraction using historical data.
Publication TypeJournal Article
Year of Publication2018
AuthorsPsioda, Matthew A., and Joseph G. Ibrahim
JournalStat Med
Volume37
Issue26
Pagination3814-3831
Date Published2018 11 20
ISSN1097-0258
KeywordsAlgorithms, Bayes Theorem, Bias, Disease-Free Survival, Humans, Melanoma, Models, Statistical, Research Design, Sample Size
Abstract

In this paper, we develop a general Bayesian clinical trial design methodology, tailored for time-to-event trials with a cured fraction in scenarios where a previously completed clinical trial is available to inform the design and analysis of the new trial. Our methodology provides a conceptually appealing and computationally feasible framework that allows one to construct a fixed, maximally informative prior a priori while simultaneously identifying the minimum sample size required for the new trial so that the design has high power and reasonable type I error control from a Bayesian perspective. This strategy is particularly well suited for scenarios where adaptive borrowing approaches are not practical due to the nature of the trial, complexity of the model, or the source of the prior information. Control of a Bayesian type I error rate offers a sensible balance between wanting to use high-quality information in the design and analysis of future trials while still controlling type I errors in an equitable way. Moreover, sample size determination based on our Bayesian view of power can lead to a more adequately sized trial by virtue of taking into account all the uncertainty in the treatment effect. We demonstrate our methodology by designing a cancer clinical trial in high-risk melanoma.

DOI10.1002/sim.7846
Alternate JournalStat Med
Original PublicationBayesian design of a survival trial with a cured fraction using historical data.
PubMed ID29938817
PubMed Central IDPMC6288795
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States
Project: