A random effects model for multivariate failure time data from multicenter clinical trials.
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.
Cai
J
J
Department of Biostatistics, The University of North Carolina at Chapel Hill, 27599-7400, USA. cai@bios.unc.edu
Sen
P K
PK
Zhou
H
H
D000806:Angiotensin-Converting Enzyme Inhibitors / Q000627:therapeutic use; D001699:Biometry; D003198:Computer Simulation; D004656:Enalapril / Q000627:therapeutic use; D005260:Female; D006333:Heart Failure / Q000188:drug therapy; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data*; D015999:Multivariate Analysis; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*
D000806:Angiotensin-Converting Enzyme Inhibitors; D004656:Enalapril