An empirical comparison of two semi-parametric approaches for the estimation of covariate effects from multivariate failure time data

Sujuan Gao, Xiao Hua Zhou

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

We conducted a simulation study to compare two semi-parametric approaches for the estimation of covariate effects from multivariate failure time data. The first approach was developed by Wei, Lin and Weissfeld (WLW) and the second by Liang, Self and Chang (LSC). Based on the simulation results we recommend Wei, Lin and Weissfeld's method for the situations with identical covariates and high correlations between the failure times. When the covariates are independent, LSC produces smaller mean squared errors than WLW, although at the expense of larger bias. We also compared four computer programs for implementing Wei, Lin and Weissfeld's approach: a FORTRAN program, MULCOX2; a SAS macro; the coxph function in S-plus, and a specialized software package for complex survey data (SUDAAN). Our comparison indicates that for large data sets, the speeds of the SAS macro and coxph are comparable, while MULCOX2 and SUDAAN took longer to run. However, MULCOX2 and coxph function in S-plus have the advantage of allowing time-dependent covariates, and SUDAAN has the advantage of handling complex survey data.

Original languageEnglish
Pages (from-to)2049-2062
Number of pages14
JournalStatistics in Medicine
Volume16
Issue number18
DOIs
StatePublished - Sep 30 1997

Fingerprint

Multivariate Failure Times
Failure Time Data
Multivariate Data
Covariates
Survey Data
D.3.2 [Programming Languages]: Language Classifications - Fortran
Software
Time-dependent Covariates
Failure Time
Mean Squared Error
Software Package
Large Data Sets
Simulation Study
Simulation
Surveys and Questionnaires

ASJC Scopus subject areas

  • Epidemiology

Cite this

An empirical comparison of two semi-parametric approaches for the estimation of covariate effects from multivariate failure time data. / Gao, Sujuan; Zhou, Xiao Hua.

In: Statistics in Medicine, Vol. 16, No. 18, 30.09.1997, p. 2049-2062.

Research output: Contribution to journalArticle

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