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Estimating Regression Parameters and Degree of Dependence for Multivariate Failure Time Data
Author(s) -
Mahé Cédric,
Chevret Sylvie
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.01078.x
Subject(s) - multivariate statistics , censoring (clinical trials) , estimator , statistics , proportional hazards model , regression , consistency (knowledge bases) , mathematics , logistic regression , covariate , blindness , robustness (evolution) , econometrics , computer science , medicine , artificial intelligence , optometry , biochemistry , chemistry , gene
Summary. Multivariate failure time data are frequently encountered in longitudinal studies when subjects may experience several events or when there is a grouping of individuals into a cluster. To take into account the dependence of the failure times within the unit (the individual or the cluster) as well as censoring, two multivariate generalizations of the Cox proportional hazards model are commonly used. The marginal hazard model is used when the purpose is to estimate mean regression parameters, while the frailty model is retained when the purpose is to assess the degree of dependence within the unit. We propose a new approach based on the combination of the two aforementioned models to estimate both these quantities. This two‐step estimation procedure is quicker and more simple to implement than the EM algorithm used in frailty models estimation. Simulation results are provided to illustrate robustness, consistency, and large‐sample properties of estimators. Finally, this method is exemplified on a diabetic retinopathy study in order to assess the effect of photocoagulation in delaying the onset of blindness as well as the dependence between the two eyes blindness times of a patient.