title: Group sequential control of overall toxicity incidents in clinical trials - non-Bayesian and Bayesian approaches.
abstract: In some small clinical trials, toxicity is not a primary endpoint; however, it often has dire effects on patients' quality of life and is even life-threatening. For such clinical trials, rigorous control of the overall incidence of adverse events is desirable, while simultaneously collecting safety information. In this article, we propose group sequential toxicity monitoring strategies to control overall toxicity incidents below a certain level as opposed to performing hypothesis testing, which can be incorporated into an existing study design based on the primary endpoint. We consider two sequential methods: a non-Bayesian approach in which stopping rules are obtained based on the 'future' probability of an excessive toxicity rate; and a Bayesian adaptation modifying the proposed non-Bayesian approach, which can use the information obtained at interim analyses. Through an extensive Monte Carlo study, we show that the Bayesian approach often provides better control of the overall toxicity rate than the non-Bayesian approach. We also investigate adequate toxicity estimation after the studies. We demonstrate the applicability of our proposed methods in controlling the symptomatic intracranial hemorrhage rate for treating acute ischemic stroke patients.
authors.affiliation: Department of Biostatistics, University at Buffalo, State University of New York, USA jinheeyu@buffalo.edu.
authors.forename: Jihnhee
authors.initials: J
authors.lastname: Yu
authors.affiliation: Department of Biostatistics, University at Buffalo, State University of New York, USA.
authors.forename: Alan D
authors.initials: AD
authors.lastname: Hutson
authors.affiliation: Department of Neurosurgery, University at Buffalo, State University of New York, USA.
authors.forename: Adnan H
authors.initials: AH
authors.lastname: Siddiqui
authors.affiliation: Department of Neurosurgery, University at Buffalo, State University of New York, USA.
authors.forename: Mary A
authors.initials: MA
authors.lastname: Kedron
mesh_terms: D001499:Bayes Theorem; D056808:Biostatistics; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D006801:Humans; D020300:Intracranial Hemorrhages / Q000209:etiology / Q000517:prevention & control; D015233:Models, Statistical; D015607:Stents / Q000009:adverse effects; D020521:Stroke / Q000628:therapy