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Lifetime Data Analysis

, Volume 12, Issue 2, pp 169–190 | Cite as

Analysis of longitudinal health-related quality of life data with terminal events

  • Zhezhen Jin
  • Mengling Liu
  • Steven Albert
  • Zhiliang Ying
Article

Abstract

Longitudinal health-related quality of life data arise naturally from studies of progressive and neurodegenerative diseases. In such studies, patients’ mental and physical conditions are measured over their follow-up periods and the resulting data are often complicated by subject-specific measurement times and possible terminal events associated with outcome variables. Motivated by the “Predictor’s Cohort” study on patients with advanced Alzheimer disease, we propose in this paper a semiparametric modeling approach to longitudinal health-related quality of life data. It builds upon and extends some recent developments for longitudinal data with irregular observation times. The new approach handles possibly dependent terminal events. It allows one to examine time-dependent covariate effects on the evolution of outcome variable and to assess nonparametrically change of outcome measurement that is due to factors not incorporated in the covariates. The usual large-sample properties for parameter estimation are established. In particular, it is shown that relevant parameter estimators are asymptotically normal and the asymptotic variances can be estimated consistently by the simple plug-in method. A general procedure for testing a specific parametric form in the nonparametric component is also developed. Simulation studies show that the proposed approach performs well for practical settings. The method is applied to the motivating example.

Keywords

Censoring Health-related quality of life Inverse probability weighting Semiparametric regression Terminal event 

Notes

Acknowledgement

The authors would like to thank the Associate Editor and two referees for their insightful and constructive comments. This research was supported in part by grants from the National Institutes of Health, the National Science Foundation and the New York City Council Speaker’s Fund for Public Health Research.

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Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Zhezhen Jin
    • 1
  • Mengling Liu
    • 2
  • Steven Albert
    • 3
  • Zhiliang Ying
    • 4
  1. 1.Department of BiostatisticsColumbia UniversityNew YorkUSA
  2. 2.Division of Biostatistics, School of MedicineNew York UniversityNew YorkUSA
  3. 3.Department of Behavioral & Community Health SciencesUniversity of PittsburghPittsburghUSA
  4. 4.Department of StatisticsColumbia UniversityNew YorkUSA

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