Lifetime Data Analysis

, Volume 21, Issue 1, pp 20–41

A flexible semiparametric transformation model for recurrent event data

Authors

  • Lin Dong
    • Institute of Applied Mathematics, Academy of Mathematics and Systems ScienceChinese Academy of Sciences
    • Institute of Applied Mathematics, Academy of Mathematics and Systems ScienceChinese Academy of Sciences
Article

DOI: 10.1007/s10985-013-9285-1

Cite this article as:
Dong, L. & Sun, L. Lifetime Data Anal (2015) 21: 20. doi:10.1007/s10985-013-9285-1
  • 238 Views

Abstract

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.

Keywords

Estimating equationMarginal modelModel checking Recurrent eventsTime-varying effectsTransformation model

Copyright information

© Springer Science+Business Media New York 2013