A class of accelerated means regression models for recurrent event data.
In this article, we propose a general class of accelerated means regression models for recurrent event data. The class includes the proportional means model, the accelerated failure time model and the accelerated rates model as special cases. The new model offers great flexibility in formulating the effects of covariates on the mean functions of counting processes while leaving the stochastic structure completely unspecified. For the inference on the model parameters, estimating equation approaches are developed and both large and final sample properties of the proposed estimators are established. In addition, some graphical and numerical procedures are presented for model checking. An illustration with multiple-infection data from a clinic study on chronic granulomatous disease is also provided.
Sun
Liuquan
L
Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, People's Republic of China. slq@amt.ac.cn
Su
Bin
B
D000367:Age Factors; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006105:Granulomatous Disease, Chronic / Q000276:immunology / Q000628:therapy; D006801:Humans; D007371:Interferon-gamma / Q000627:therapeutic use; D008954:Models, Biological; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012044:Regression Analysis
D007371:Interferon-gamma