Additive transformation models for clustered failure time data.
We propose a class of additive transformation risk models for clustered failure time data. Our models are motivated by the usual additive risk model for independent failure times incorporating a frailty with mean one and constant variability which is a natural generalization of the additive risk model from univariate failure time to multivariate failure time. An estimating equation approach based on the marginal hazards function is proposed. Under the assumption that cluster sizes are completely random, we show the resulting estimators of the regression coefficients are consistent and asymptotically normal. We also provide goodness-of-fit test statistics for choosing the transformation. Simulation studies and real data analysis are conducted to examine the finite-sample performance of our estimators.
Zeng
Donglin
D
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. dzeng@bios.unc.edu
Cai
Jianwen
J
D002986:Clinical Trials as Topic; D016000:Cluster Analysis; D003198:Computer Simulation; D003930:Diabetic Retinopathy / Q000628:therapy; D006801:Humans; D008028:Light Coagulation / Q000592:standards; D015233:Models, Statistical