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Bayesian design of a survival trial with a cured fraction using historical data
Author(s) -
Psioda Matthew A.,
Ibrahim Joseph G.
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7846
Subject(s) - sample size determination , computer science , bayesian probability , type i and type ii errors , clinical trial , fraction (chemistry) , clinical study design , a priori and a posteriori , statistical power , data mining , machine learning , statistics , artificial intelligence , mathematics , medicine , chemistry , organic chemistry , pathology , philosophy , epistemology
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.