title: Early stopping rules in clinical trials based on sequential monitoring of serious adverse events.
abstract: Several multistage or group sequential statistical methods have been developed for defining stopping rules in terms of efficacy in phase II and III clinical trials, but they rely on interim analyses after the inclusion of a fixed number of patients. These methods, however, need to be adapted for the evaluation of serious adverse events (SAEs), which can occur relatively early in the trial. A high frequency of their occurrence may require the trial to close early. The methods developed here define stopping rules after the occurrence of each SAE by comparing the number of patients included to the number of patients satisfying maximum SAE criteria. The nominal type I error, power, and average sample number (ASN) under specific hypotheses are obtained through simulations. Data from a clinical trial are presented as an example.
authors.affiliation: Unité de Biostatistique, CRLC Val d'Aurelle-Paul Lamarque, Montpellier cedex 5, France. andrew.kramar@valdorel.fnclcc.fr
authors.forename: Andrew
authors.initials: A
authors.lastname: Kramar
authors.forename: C
authors.initials: C
authors.lastname: Bascoul-Mollevi
mesh_terms: D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic; D017326:Clinical Trials, Phase III as Topic; D006801:Humans; D008991:Monitoring, Physiologic; D018401:Sample Size; D013812:Therapeutics / Q000009:adverse effects*