Semiparametric estimation for the additive hazards model with left-truncated and right-censored data

Chiung Yu Huang, Jing Qin

Research output: Contribution to journalArticle

Abstract

Survival data from prevalent cases collected under a cross-sectional sampling scheme are subject to left-truncation. When fitting an additive hazards model to left-truncated data, the conditional estimating equation method (Lin & Ying, 1994), obtained by modifying the risk sets to account for left-truncation, can be very inefficient, as the marginal likelihood of the truncation times is not used in the estimation procedure. In this paper, we use a pairwise pseudolikelihood to eliminate nuisance parameters from the marginal likelihood and, by combining the marginal pairwise pseudo-score function and the conditional estimating function, propose an efficient estimator for the additive hazards model. The proposed estimator is shown to be consistent and asymptotically normally distributed with a sandwich-type covariance matrix that can be consistently estimated. Simulation studies show that the proposed estimator is more efficient than its competitors. A data analysis illustrates application of the method.

Original languageEnglish (US)
Pages (from-to)877-888
Number of pages12
JournalBiometrika
Volume100
Issue number4
DOIs
StatePublished - Dec 1 2013

Keywords

  • Canadian Study of Health and Aging
  • Composite likelihood
  • Estimating equation
  • Martingale
  • Prevalent sampling

ASJC Scopus subject areas

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

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