Sample size reestimation and Bayesian predictive probability for single-arm clinical trials with a time-to-event endpoint using Weibull distribution with unknown shape parameter

• Published in: Journal of biopharmaceutical statistics Vol. 34; no. 4; pp. 469 - 487
• Main Authors: Waleed, Muhammad, He, Jianghua, Phadnis, Milind A.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 03.07.2024; Taylor & Francis Ltd
• Subjects: Bayes Theorem; Clinical trials; Clinical Trials as Topic - methods; Clinical Trials as Topic - statistics & numerical data; Computer Simulation; Data Interpretation, Statistical; Endpoint Determination - methods; Endpoint Determination - statistics & numerical data; Humans; Interim analysis; Models, Statistical; Pilot Projects; prior elicitation; Probability; Research Design - statistics & numerical data; Sample Size; sample size re-estimation; stochastic curtailment; Time Factors; Weibull distribution; stochastic curtailment; Interim analysis; sample size re-estimation; Weibull distribution; prior elicitation
• ISSN: 1054-3406; 1520-5711
• DOI: 10.1080/10543406.2023.2234998

This manuscript consists of two topics. Firstly, we explore the utility of internal pilot study (IPS) approach for reestimating sample size at an interim stage when a reliable estimate of the nuisance shape parameter of the Weibull distribution for modeling survival data is unavailable during the planning phase of a study. Although IPS approach can help rescue the study power, it is noted that the adjusted sample size can be as much as twice the initially planned sample size, which may put substantial practical constraints to continue the study. Secondly, we discuss Bayesian predictive probability for conducting interim analyses to obtain preliminary evidence of efficacy or futility of an experimental treatment warranting early termination of a clinical trial. In the context of single-arm clinical trials with time-to-event endpoints following Weibull distribution, we present the calculation of the Bayesian predictive probability when the shape parameter of the Weibull distribution is unknown. Based on the data accumulated at the interim, we propose two approaches which rely on the posterior mode or the entire posterior distribution of the shape parameter. To account for uncertainty in the shape parameter, it is recommended to incorporate its entire posterior distribution in our calculation.