Non-parametric paired two-sample tests for censored survival data incorporating longitudinal covariate information

Shari Messinger, Susan Murray

Research output: Contribution to journalArticle

Abstract

In this manuscript, we present non-parametric two-sample tests for paired censored survival data incorporating longitudinal covariate information. These tests take advantage of information collected at baseline and post-baseline to provide efficiency gains when censoring is uninformative. Additionally, these methods adjust for potential bias from informative censoring that is captured by the baseline and longitudinal covariates. Finite sample properties are investigated with simulation, and we illustrate methodology with an example from the Early Treatment Diabetic Retinopathy Study.

Original languageEnglish
Pages (from-to)301-318
Number of pages18
JournalStatistics in Medicine
Volume24
Issue number2
DOIs
StatePublished - Jan 30 2005

Fingerprint

Censored Survival Data
Two-sample Test
Diabetic Retinopathy
Covariates
Baseline
Informative Censoring
Non-parametric test
Censoring
Methodology
Simulation

Keywords

  • Correlated survival
  • Kaplan-Meier estimate
  • Longitudinal covariates
  • Pepe-Fleming test
  • Selection bias

ASJC Scopus subject areas

  • Epidemiology

Cite this

Non-parametric paired two-sample tests for censored survival data incorporating longitudinal covariate information. / Messinger, Shari; Murray, Susan.

In: Statistics in Medicine, Vol. 24, No. 2, 30.01.2005, p. 301-318.

Research output: Contribution to journalArticle

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