Factorial analyses of treatment effects under independent right-censoring

Dennis Dobler, Markus Pauly

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This paper introduces new effect parameters for factorial survival designs with possibly right-censored time-to-event data. In the special case of a two-sample design, it coincides with the concordance or Wilcoxon parameter in survival analysis. More generally, the new parameters describe treatment or interaction effects and we develop estimates and tests to infer their presence. We rigorously study their asymptotic properties and additionally suggest wild bootstrapping for a consistent and distribution-free application of the inference procedures. The small sample performance is discussed based on simulation results. The practical usefulness of the developed methodology is exemplified on a data example about patients with colon cancer by conducting one- and two-factorial analyses.

LanguageEnglish
Pages1-19
Number of pages19
JournalStatistical Methods in Medical Research
Early online date5 Mar 2019
DOIs
Publication statusE-pub ahead of print - 5 Mar 2019

Fingerprint

Right Censoring
Treatment Effects
Factorial
Survival Analysis
Colonic Neoplasms
Survival
Interaction Effects
Concordance
Distribution-free
Bootstrapping
Small Sample
Asymptotic Properties
Cancer
Therapeutics
Methodology
Estimate
Simulation
Design

Keywords

  • Factorial designs
  • Kaplan–Meier estimator
  • nonparametric statistics
  • quadratic forms
  • wild bootstrap

Cite this

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Factorial analyses of treatment effects under independent right-censoring. / Dobler, Dennis; Pauly, Markus.

In: Statistical Methods in Medical Research, 05.03.2019, p. 1-19.

Research output: Contribution to JournalArticleAcademicpeer-review

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