Factorial analyses of treatment effects under independent right-censoring

Dennis Dobler*, Markus Pauly

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalStatistical Methods in Medical Research
Issue number2
Early online date5 Mar 2019
Publication statusPublished - 2020


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


Dive into the research topics of 'Factorial analyses of treatment effects under independent right-censoring'. Together they form a unique fingerprint.

Cite this