title: Predicting analysis time in events-driven clinical trials using accumulating time-to-event surrogate information.
abstract: For clinical trials with time-to-event endpoints, predicting the accrual of the events of interest with precision is critical in determining the timing of interim and final analyses. For example, overall survival (OS) is often chosen as the primary efficacy endpoint in oncology studies, with planned interim and final analyses at a pre-specified number of deaths. Often, correlated surrogate information, such as time-to-progression (TTP) and progression-free survival, are also collected as secondary efficacy endpoints. It would be appealing to borrow strength from the surrogate information to improve the precision of the analysis time prediction. Currently available methods in the literature for predicting analysis timings do not consider utilizing the surrogate information. In this article, using OS and TTP as an example, a general parametric model for OS and TTP is proposed, with the assumption that disease progression could change the course of the overall survival. Progression-free survival, related both to OS and TTP, will be handled separately, as it can be derived from OS and TTP. The authors seek to develop a prediction procedure using a Bayesian method and provide detailed implementation strategies under certain assumptions. Simulations are performed to evaluate the performance of the proposed method. An application to a real study is also provided. Copyright © 2015 John Wiley & Sons, Ltd.
authors.affiliation: Biometrics and Data Operations, Celgene Corporation, Summit, NJ, USA.
authors.forename: Jianming
authors.initials: J
authors.lastname: Wang
authors.affiliation: Global Biostatistics Sciences, Amgen, Thousand Oaks, CA, USA.
authors.forename: Chunlei
authors.initials: C
authors.lastname: Ke
authors.affiliation: Biometrics and Data Operations, Celgene Corporation, Summit, NJ, USA.
authors.forename: Zhinuan
authors.initials: Z
authors.lastname: Yu
authors.affiliation: Biometrics and Data Operations, Celgene Corporation, Summit, NJ, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Fu
authors.affiliation: Biometrics and Data Operations, Celgene Corporation, Summit, NJ, USA.
authors.forename: Bruce
authors.initials: B
authors.lastname: Dornseif
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D018450:Disease Progression; D018572:Disease-Free Survival; D023381:Endpoint Determination; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms; D012107:Research Design; D015996:Survival Rate; D013997:Time Factors
keywords: analysis time prediction; event driven; overall survival; surrogate information; time-to-progression