Partially linear hazard regression for multivariate survival data

Jianwen Cai, Jianqing Fan, Jiancheng Jiang, Haibo Zhou

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

This article studies estimation of partially linear hazard regression models for multivariate survival data. A profile pseudo-partial likelihood estimation method is proposed under the marginal hazard model framework. The estimation on the parameters for the linear part is accomplished by maximization of a pseudo-partial likelihood profiled over the nonparametric part. This enables us to obtain √o-consistent estimators of the parametric component. Asymptotic normality is obtained for the estimates of both the linear and nonlinear parts. The new technical challenge is that the nonparametric component is indirectly estimated through its integrated derivative function from a local polynomial fit. An algorithm of fast implementation of our proposed method is presented. Consistent standard error estimates using sandwich-type ideas are also developed, which facilitates inferences for the model. It is shown that the nonparametric component can be estimated as well as if the parametric components were known and the failure times within each subject were independent. Simulations are conducted to demonstrate the performance of the proposed method. A real dataset is analyzed to illustrate the proposed methodology.

Original languageEnglish (US)
Pages (from-to)538-551
Number of pages14
JournalJournal of the American Statistical Association
Volume102
Issue number478
DOIs
StatePublished - Jun 1 2007

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Local pseudo-partial likelihood
  • Marginal hazard model
  • Multivariate failure time
  • Partially linear
  • Profile pseudo-partial likelihood

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