Conditional independence test of failure and truncation times: Essential tool for method selection

Jing Ning, Daewoo Pak, Hong Zhu, Jing Qin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Conditional independence assumption of truncation and failure times conditioning on covariates is a fundamental and common assumption in the regression analysis of left-truncated and right-censored data. Testing for this assumption is essential to ensure the correct inference on the failure time, but this has often been overlooked in the literature. With consideration of challenges caused by left truncation and right censoring, tests for this conditional independence assumption are developed in which the generalized odds ratio derived from a Cox proportional hazards model on the failure time and the concept of Kendall's tau are combined. Except for the Cox proportional hazards model, no additional model assumptions are imposed, and the distributions of the truncation time and conditioning variables are unspecified. The asymptotic properties of the test statistic are established and an easy implementation for obtaining its distribution is developed. The performance of the proposed test has been evaluated through simulation studies and two real studies.

Original languageEnglish (US)
Article number107402
JournalComputational Statistics and Data Analysis
StatePublished - Apr 2022


  • Conditional generalized odds ratio
  • Conditional independence
  • Cox proportional hazards model
  • Left truncation
  • Right censoring

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


Dive into the research topics of 'Conditional independence test of failure and truncation times: Essential tool for method selection'. Together they form a unique fingerprint.

Cite this