Instrumental variable with competing risk model

Cheng Zheng, Ran Dai, Parameswaran N. Hari, Mei Jie Zhang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with competing risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured covariates. Instrumental variable methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. On the basis of a semiparametric additive hazard model for the subdistribution hazard, we propose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confoundings for competing risk settings. We derived the asymptotic properties for the proposed estimator. The estimator is shown to be well performed under finite sample size according to simulation results. We applied our method to a real transplant data example and showed that the unmeasured confoundings lead to significant bias in the estimation of the effect (about 50% attenuated).

Original languageEnglish (US)
Pages (from-to)1240-1255
Number of pages16
JournalStatistics in Medicine
Issue number8
StatePublished - Apr 15 2017
Externally publishedYes


  • additive hazard model
  • competing risk
  • instrumental variable
  • survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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