title: A general class of semiparametric transformation frailty models for nonproportional hazards survival data.
abstract: We propose a semiparametrically efficient estimation of a broad class of transformation regression models for nonproportional hazards data. Classical transformation models are to be viewed from a frailty model paradigm, and the proposed method provides a unified approach that is valid for both continuous and discrete frailty models. The proposed models are shown to be flexible enough to model long-term follow-up survival data when the treatment effect diminishes over time, a case for which the PH or proportional odds assumption is violated, or a situation in which a substantial proportion of patients remains cured after treatment. Estimation of the link parameter in frailty distribution, considered to be unknown and possibly dependent on a time-independent covariates, is automatically included in the proposed methods. The observed information matrix is computed to evaluate the variances of all the parameter estimates. Our likelihood-based approach provides a natural way to construct simple statistics for testing the PH and proportional odds assumptions for usual survival data or testing the short- and long-term effects for survival data with a cure fraction. Simulation studies demonstrate that the proposed inference procedures perform well in realistic settings. Applications to two medical studies are provided.
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. schoi6@mdanderson.org
authors.forename: Sangbum
authors.initials: S
authors.lastname: Choi
authors.forename: Xuelin
authors.initials: X
authors.lastname: Huang
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D016016:Proportional Hazards Models; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D016019:Survival Analysis; D015996:Survival Rate