Semiparametric efficiency and its implication on the design and analysis of group-sequential studies

Daniel O. Scharfstein, Anastasios A. Tsiatis, James M. Robins

Research output: Contribution to journalArticle

Abstract

Authors have shown that the time-sequential joint distributions of many statistics used to analyze data arising from group-sequential time-to-event and longitudinal studies are multivariate normal with an independent increments covariance structure. In Theorem 1 of this article, we demonstrate that this limiting distribution arises naturally when one uses an efficient test statistic to test a single parameter in a semiparametric or parametric model. Because we are able to think of many of the statistics in the literature in this fashion, the limiting distribution under investigation is just a special case of Theorem 1. Using this general structure, we then develop an information-based design and monitoring procedure that can be applied to any type of model for any type of group-sequential study provided that there is a unique parameter of interest that can be efficiently tested.

Original languageEnglish (US)
Pages (from-to)1342-1350
Number of pages9
JournalJournal of the American Statistical Association
Volume92
Issue number440
DOIs
StatePublished - Dec 1 1997

Keywords

  • Independent increment
  • Information-based design and monitoring
  • Longitudinal study
  • Maximum information trial
  • Time-to-event study

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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