Estimation in the Cox proportional hazards model with left-truncated and interval-censored data

W. Pan, R. Chappell

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

We show that the nonparametric maximum likelihood estimate, (NPMLE) of the regression coefficient from the joint likelihood (of the regression coefficient and the baseline survival) works well for the Cox proportional hazards model with left-truncated and interval-censored data, but the NPMLE may underestimate the baseline survival. Two alternatives are also considered: first, the marginal likelihood approach by extending Satten (1996, Biometrika 83, 355-370) to truncated data, where the baseline distribution is eliminated as a nuisance parameter; and second, the monotone maximum likelihood estimate that maximizes the joint likelihood by assuming that the baseline distribution has a nondecreasing hazard function, which was originally proposed to overcome the underestimation of the survival from the NPMLE for left-truncated data without covariates (Tsai, 1988, Biometrika 75, 319-324). The bootstrap is proposed to draw inference. Simulations were conducted to assess their performance. The methods are applied to the Massachusetts Health Care Panel Study data set to compare the probabilities of losing functional independence for male and female seniors.

Original languageEnglish (US)
Pages (from-to)64-70
Number of pages7
JournalBiometrics
Volume58
Issue number1
DOIs
StatePublished - 2002

Keywords

  • Bootstrap
  • Gibbs sampler
  • Gradient projection
  • Joint likelihood
  • Monotone MLE
  • NPMLE

Fingerprint Dive into the research topics of 'Estimation in the Cox proportional hazards model with left-truncated and interval-censored data'. Together they form a unique fingerprint.

Cite this