A flexible semiparametric transformation model for recurrent event data.
In this article, we propose a class of semiparametric transformation models for recurrent event data, in which the baseline mean function is allowed to depend on covariates through an additive model, and some covariate effects are allowed to be time-varying. For inference on the model parameters, estimating equation approaches are developed, and the asymptotic properties of the resulting estimators are established. In addition, a lack-of-fit test is presented to assess the adequacy of the model. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a bladder cancer study is illustrated.
Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing , 100190, People's Republic of China.
Lin
L
Dong
Liuquan
L
Sun
D003198:Computer Simulation; D006801:Humans; D015993:Life Tables; D055641:Mathematical Concepts; D015233:Models, Statistical; D015999:Multivariate Analysis; D009364:Neoplasm Recurrence, Local / Q000188:drug therapy / Q000473:pathology; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012008:Recurrence; D018709:Statistics, Nonparametric; D001749:Urinary Bladder Neoplasms / Q000188:drug therapy / Q000473:pathology