A GEE approach for the semiparametric accelerated lifetime model with multivariate interval-censored data

Maru Kim, Sangbum Choi

Research output: Contribution to journalArticlepeer-review

Abstract

Multivariate or clustered failure time data often occur in many medical, epidemiological, and socio-economic studies when survival data are collected from several research centers. If the data are periodically observed as in a longitudinal study, survival times are often subject to various types of interval-censoring, creating multivariate interval-censored data. Then, the event times of interest may be correlated among individuals who come from the same cluster. In this article, we propose a unified linear regression method for analyzing multivariate interval-censored data. We consider a semiparametric multivariate accelerated failure time model as a statistical analysis tool and develop a generalized Buckley-James method to make inferences by imputing interval-censored observations with their conditional mean values. Since the study population consists of several heterogeneous clusters, where the subjects in the same cluster may be related, we propose a generalized estimating equations approach to accommodate potential dependence in clusters. Our simulation results confirm that the proposed estimator is robust to misspecification of working covariance matrix and statistical efficiency can increase when the working covariance structure is close to the truth. The proposed method is applied to the dataset from a diabetic retinopathy study.

Original languageEnglish
Pages (from-to)389-402
Number of pages14
JournalCommunications for Statistical Applications and Methods
Volume30
Issue number4
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.

Keywords

  • Buckley-James method
  • accelerated failure time model
  • clustered data
  • generalized estimating equation
  • interval-censored data
  • survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Finance
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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