Literature DB >> 11169597

Detecting treatment-by-centre interaction in multi-centre clinical trials.

R F Potthoff1, B L Peterson, S L George.   

Abstract

This paper considers several permutation tests for treatment-by-centre interaction in multi-centre clinical trials in which the endpoint is survival time subject to censoring. Some of the tests are based on existing tests and some are new. To evaluate and compare the tests with respect to power under different conditions, we generated survival times and censoring times through simulation. We used special methodology to handle the unusual problems that arise in power simulations when the tests under study are permutation tests. Different conditions yielded different interaction tests as the best performers. Although one test gave comparatively good power under almost all conditions, some of the other tests also appear to be useful. For the sample sizes and configurations in the simulations, power is generally low; thus it may not be possible to detect interaction reliably when it exists, a finding in agreement with the known low power for interaction tests in general. Copyright 2001 John Wiley & Sons, Ltd.

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Year:  2001        PMID: 11169597     DOI: 10.1002/1097-0258(20010130)20:2<193::aid-sim651>3.0.co;2-#

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  7 in total

1.  A small sample study of the STEPP approach to assessing treatment-covariate interactions in survival data.

Authors:  Marco Bonetti; David Zahrieh; Bernard F Cole; Richard D Gelber
Journal:  Stat Med       Date:  2009-04-15       Impact factor: 2.373

2.  Multiple testing of treatment-effect-modifying biomarkers in a randomized clinical trial with a survival endpoint.

Authors:  Stefan Michiels; Richard F Potthoff; Stephen L George
Journal:  Stat Med       Date:  2011-02-23       Impact factor: 2.373

3.  Sample Size Requirements and Study Duration for Testing Main Effects and Interactions in Completely Randomized Factorial Designs When Time to Event is the Outcome.

Authors:  Barry Kurt Moser; Susan Halabi
Journal:  Commun Stat Theory Methods       Date:  2015       Impact factor: 0.893

4.  MULTI-CENTER CLINICAL TRIALS: RANDOMIZATION AND ANCILLARY STATISTICS.

Authors:  L U Zheng; Marvin Zelen
Journal:  Ann Appl Stat       Date:  2008-07-03       Impact factor: 2.083

5.  Permutation Testing for Treatment-Covariate Interactions and Subgroup Identification.

Authors:  Jared C Foster; Bin Nan; Lei Shen; Niko Kaciroti; Jeremy M G Taylor
Journal:  Stat Biosci       Date:  2015-03-05

6.  Subpopulation Treatment Effect Pattern Plot (STEPP) analysis for continuous, binary, and count outcomes.

Authors:  Wai-Ki Yip; Marco Bonetti; Bernard F Cole; William Barcella; Xin Victoria Wang; Ann Lazar; Richard D Gelber
Journal:  Clin Trials       Date:  2016-04-19       Impact factor: 2.486

7.  Identifying treatment effect heterogeneity in clinical trials using subpopulations of events: STEPP.

Authors:  Ann A Lazar; Marco Bonetti; Bernard F Cole; Wai-Ki Yip; Richard D Gelber
Journal:  Clin Trials       Date:  2015-10-22       Impact factor: 2.486

  7 in total

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