A flexible additive-multiplicative transformation mean model for recurrent event data

Abstract

Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a flexible semi-parametric additive-multiplicative transformation mean model for recurrent event data, which includes the multiplicative model and additive transformation model as special cases. The new model is flexible in that they allow for both additive and multiplicative covariates effects, and additive effects are allowed to be time-varying. The estimation of regression parameters in the model is given by using the idea of estimating equations, and the asymptotic properties of the resulting estimators are established. Numerical studies under different settings were conducted for assessing the proposed methodology and an application to a bladder cancer study is illustrated. The results suggest that they work well.

KEYWORDS:

• Recurrent event data
• additive-multiplicative effects
• mean model
• transformation model
• estimating equations

Additional information

Funding

This work was supported by the Construct Program of the key Discipline in Hunan Province and the National Natural Science Foundation of China (grant number 81773530).