© 2021 The International Biometric Society. Group sequential testing procedures are widely employed in long-term clinical trials. In clinical trials, group sequential methods are used to stop trials early for either lack of efficacy or for safety reasons, or if medication is found to be highly effective.
The goal was to waste as little ammunition as possible during testing. Real study examples are taken to demonstrate our method.Īlpha spending function dependent increment expected sample size intracluster correlation coefficient log-rank test variable cluster size. It was used extensively in quality assurance testing during the war. In some clinical trials, the sample size may not be fixed but allowed to be determined as the data are collected. Extensive simulation studies are conducted to evaluate the performance of our proposed testing procedure using some existing alpha spending functions in terms of expected sample size and maximal sample size. GROUP SEQUENTIAL METHODS In the hypothesis testing problems that we have studied, the critical value of the test statistic and the power of the test are based on predetermined sample sizes. We also propose an information fraction for group sequential trials with clustered survival data and a corresponding sample size determination approach. We prove that the sequentially computed log-rank statistics in CRTs do not have independent increment property. If adaptation has occurred at an analysis <, replace +1,( ) in () by the new ෨ ( +1),, ෨( ) and apply the original group sequential testing boundary. The key to this approach is determining the joint distribution of test statistics and the information fraction at the time of interim analysis. The aim of this study is comparing group sequential tests in respect to advantage of sample size reduction and early termination. The alpha spending function approach is used for sequential data monitoring. We propose group sequential methods for cluster randomized trials (CRTs) with time-to-event endpoint.
0 kommentar(er)
