title: Gamma frailty transformation models for multivariate survival times.
abstract: We propose a class of transformation models for multivariate failure times. The class of transformation models generalize the usual gamma frailty model and yields a marginally linear transformation model for each failure time. Nonparametric maximum likelihood estimation is used for inference. The maximum likelihood estimators for the regression coefficients are shown to be consistent and asymptotically normal, and their asymptotic variances attain the semiparametric efficiency bound. Simulation studies show that the proposed estimation procedure provides asymptotically efficient estimates and yields good inferential properties for small sample sizes. The method is illustrated using data from a cardiovascular study.
authors.affiliation: Department of Biostatistics, University of North Carolina, 3105-D McGavran-Greenberg Hall, Campus Box 7420, Chapel Hill, North Carolina, 27516, U.S.A., dzeng@bios.unc.edu.
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
authors.affiliation: Department of Biostatistics, Vanderbilt University, 1161 21st Avenue South, S-2323 Medical Center North, Nashville, Tennessee, 37232, U.S.A., cindy.chen@vanderbilt.edu.
authors.forename: Qingxia
authors.initials: Q
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, University of North Carolina, 3109 McGavran-Greenberg Hall, Campus Box 7420, Chapel Hill, North Carolina, 27516, U.S.A., ibrahim@bios.unc.edu.
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
keywords: Gamma frailty model; Linear transformation model; Proportional hazards model; Semiparametric efficiency