A new approach to estimation of the proportional hazards model based on interval-censored data with missing covariates

Ruiwen Zhou, Huiqiong Li, Jianguo Sun, Niansheng Tang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This paper discusses the fitting of the proportional hazards model to interval-censored failure time data with missing covariates. Many authors have discussed the problem when complete covariate information is available or the missing is completely at random. In contrast to this, we will focus on the situation where the missing is at random. For the problem, a sieve maximum likelihood estimation approach is proposed with the use of I-spline functions to approximate the unknown cumulative baseline hazard function in the model. For the implementation of the proposed method, we develop an EM algorithm based on a two-stage data augmentation. Furthermore, we show that the proposed estimators of regression parameters are consistent and asymptotically normal. The proposed approach is then applied to a set of the data concerning Alzheimer Disease that motivated this study.

Original languageEnglish
Pages (from-to)335-355
Number of pages21
JournalLifetime Data Analysis
Volume28
Issue number3
DOIs
StatePublished - Jul 2022

Keywords

  • Case II interval-censored data
  • EM algorithm
  • Missing at random
  • Sieve approach

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