Incorporating correlation for multivariate failure time data when cluster size is large

L. Xue, L. Wang, A. Qu

Research output: Contribution to journalArticle

Abstract

We propose a new estimation method for multivariate failure time data using the quadratic inference function (QIF) approach. The proposed method efficiently incorporates within-cluster correlations. Therefore, it is more efficient than those that ignore within-cluster correlation. Furthermore, the proposed method is easy to implement. Unlike the weighted estimating equations in Cai and Prentice (1995, Biometrika 82, 151-164), it is not necessary to explicitly estimate the correlation parameters. This simplification is particularly useful in analyzing data with large cluster size where it is difficult to estimate intracluster correlation. Under certain regularity conditions, we show the consistency and asymptotic normality of the proposed QIF estimators. A chi-squared test is also developed for hypothesis testing. We conduct extensive Monte Carlo simulation studies to assess the finite sample performance of the proposed methods. We also illustrate the proposed methods by analyzing primary biliary cirrhosis (PBC) data.

Original languageEnglish (US)
Pages (from-to)393-404
Number of pages12
JournalBiometrics
Volume66
Issue number2
DOIs
StatePublished - Jun 1 2010

Keywords

  • Chi-squared test
  • Correlated failure times
  • Cox's model
  • Generalized estimating equation
  • Marginal hazard rate
  • Quadratic inference function

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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