title: Bayesian design of superiority clinical trials for recurrent events data with applications to bleeding and transfusion events in myelodyplastic syndrome.
abstract: In many biomedical studies, patients may experience the same type of recurrent event repeatedly over time, such as bleeding, multiple infections and disease. In this article, we propose a Bayesian design to a pivotal clinical trial in which lower risk myelodysplastic syndromes (MDS) patients are treated with MDS disease modifying therapies. One of the key study objectives is to demonstrate the investigational product (treatment) effect on reduction of platelet transfusion and bleeding events while receiving MDS therapies. In this context, we propose a new Bayesian approach for the design of superiority clinical trials using recurrent events frailty regression models. Historical recurrent events data from an already completed phase 2 trial are incorporated into the Bayesian design via the partial borrowing power prior of Ibrahim et al. (2012, Biometrics 68, 578-586). An efficient Gibbs sampling algorithm, a predictive data generation algorithm, and a simulation-based algorithm are developed for sampling from the fitting posterior distribution, generating the predictive recurrent events data, and computing various design quantities such as the type I error rate and power, respectively. An extensive simulation study is conducted to compare the proposed method to the existing frequentist methods and to investigate various operating characteristics of the proposed design.
authors.affiliation: Department of Statistics, University of Connecticut, 215 Glenbrook Road, U-4120, Storrs, Connecticut 06269, U.S.A.
authors.forename: Ming-Hui
authors.initials: MH
authors.lastname: Chen
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
authors.forename: Kuolung
authors.initials: K
authors.lastname: Hu
authors.forename: Catherine
authors.initials: C
authors.lastname: Jia
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001803:Blood Transfusion / Q000706:statistics & numerical data*; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006470:Hemorrhage / Q000453:epidemiology / Q000517:prevention & control*; D006801:Humans; D015233:Models, Statistical; D009190:Myelodysplastic Syndromes / Q000453:epidemiology / Q000628:therapy*; D017063:Outcome Assessment, Health Care / Q000379:methods; D015995:Prevalence; D012008:Recurrence; D015203:Reproducibility of Results; D012107:Research Design; D012307:Risk Factors; D018401:Sample Size; D012680:Sensitivity and Specificity
keywords: Clinical trial design; Gibbs sampling; Myelodysplastic syndrome; Power prior; Recurrent events; Type I error rate and power