Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2020

Independent increments in group sequential tests: a review

  • Identification data

    Identifier: RP:4684
    Handle: https://hdl.handle.net/20.500.11797/RP4684
    Authors:
    Tsiatis, Anastasios A.Kim, KyungMann
    Abstract:
    In order to apply group sequential methods for interim analysis for early stopping in clinical trials, the joint distribution of test statistics over time has to be known. Often the distribution is multivariate normal or asymptotically so, and an application of group sequential methods requires multivariate integration to determine the group sequential boundaries. However, if the increments between successive test statistics are independent, the multivariate integration reduces to a univariate integration involving simple recursion based on convolution. This allows application of standard group sequential methods. In this paper we review group sequential methods and the development that established independent increments in test statistics for the primary outcomes of longitudinal or failure time data.

  • Others:

    Author, as appears in the article.: Tsiatis, Anastasios A. Kim, KyungMann
    Keywords: Failure time data
    Abstract: In order to apply group sequential methods for interim analysis for early stopping in clinical trials, the joint distribution of test statistics over time has to be known. Often the distribution is multivariate normal or asymptotically so, and an application of group sequential methods requires multivariate integration to determine the group sequential boundaries. However, if the increments between successive test statistics are independent, the multivariate integration reduces to a univariate integration involving simple recursion based on convolution. This allows application of standard group sequential methods. In this paper we review group sequential methods and the development that established independent increments in test statistics for the primary outcomes of longitudinal or failure time data.
    Journal publication year: 2020
    Publication Type: Invited article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article