Estimating the quality-of-life-adjusted gap time distribution of successive events subject to censoring

Adin Cristian Andrei*, Susan Murray

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


When treatment effects are studied in the context of successive or recurrent life events, separate analyses of the quality-of-life scores and of the inter-event, gap, times might lead to possibly contradictory conclusions. In an attempt to reconcile this, we propose a unitary and more comprehensive nonparametric analysis that combines the two separate analyses by introducing the quality-of-life-adjusted gap time concept. Inverse probability of censoring estimators of the quality-of-life-adjusted gap time joint and conditional distributions are proposed and are shown to be consistent and asymptotically normal. Simulations performed in a variety of scenarios indicate that the joint and conditional quality-of-life-adjusted gap time distribution estimators are virtually unbiased, with properly estimated standard errors and asymptotic normality features. An example from the International Breast Cancer Study Group Trial V illustrates the use of the proposed estimators.

Original languageEnglish (US)
Pages (from-to)343-355
Number of pages13
Issue number2
StatePublished - Jun 2006


  • Gap time
  • Inverse weighting
  • Nonparametric
  • Quality-of-life
  • Recurrent
  • Survival

ASJC Scopus subject areas

  • Statistics and Probability
  • General Mathematics
  • Agricultural and Biological Sciences (miscellaneous)
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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