A class of Box-Cox transformation models  for recurrent event data.
In this article, we propose a class of Box-Cox transformation models for recurrent event data, which includes the proportional means models 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 proposed models, we apply a profile pseudo-partial likelihood method to estimate the model parameters via estimating equation approaches and establish large sample properties of the estimators and examine its performance in moderate-sized samples through simulation studies. In addition, some graphical and numerical procedures are presented for model checking. An example of application on a set of multiple-infection data taken from a clinic study on chronic granulomatous disease (CGD) is also illustrated.
Sun
Liuquan
L
Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China.
Tong
Xingwei
X
Zhou
Xian
X
D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006105:Granulomatous Disease, Chronic / Q000276:immunology; D006801:Humans; D007239:Infections / Q000382:microbiology; D007371:Interferon-gamma / Q000008:administration & dosage / Q000494:pharmacology; D008954:Models, Biological; D015233:Models, Statistical; D012008:Recurrence
D007371:Interferon-gamma
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