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

Marco Bonetti, David Zahrieh, Bernard F. Cole, Richard D. Gelber

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

A new, intuitive method has recently been proposed to explore treatment-covariate interactions in survival data arising from two treatment arms of a clinical trial. The method is based on constructing overlapping subpopulations of patients with respect to one (or more) covariates of interest and in observing the pattern of the treatment effects estimated across the subpopulations. A plot of these treatment effects is called a subpopulation treatment effect pattern plot. Here, we explore the small sample characteristics of the asymptotic results associated with the method and develop an alternative permutation distribution-based approach to inference that should be preferred for smaller sample sizes. We then describe an extension of the method to the case in which the pattern of estimated quantiles of survivor functions is of interest.

Original languageEnglish (US)
Pages (from-to)1255-1268
Number of pages14
JournalStatistics in Medicine
Volume28
Issue number8
DOIs
StatePublished - Apr 15 2009

Keywords

  • Clinical trials
  • Permutation-based inference
  • Survival analysis
  • Treatment-covariate interaction

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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