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A Random Effects Model for Multivariate Failure Time Data from Multicenter Clinical Trials
Author(s) -
Cai Jianwen,
Sen Pranab K.,
Zhou Haibo
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.1999.00182.x
Subject(s) - estimator , statistics , multivariate statistics , variance (accounting) , mathematics , random effects model , hazard ratio , independence (probability theory) , population , econometrics , confidence interval , medicine , meta analysis , accounting , environmental health , business
Summary. A random effects model for analyzing multivariate failure time data is proposed. The work is motivated by the need for assessing the mean treatment effect in a multicenter clinical trial study, assuming that the centers are a random sample from an underlying population. An estimating equation for the mean hazard ratio parameter is proposed. The proposed estimator is shown to be consistent and asymptotically normally distributed. A variance estimator, based on large sample theory, is proposed. Simulation results indicate that the proposed estimator performs well in finite samples. The proposed variance estimator effectively corrects the bias of the naive variance estimator, which assumes independence of individuals within a group. The methodology is illustrated with a clinical trial data set from the Studies of Left Ventricular Dysfunction. This shows that the variability of the treatment effect is higher than found by means of simpler models.