|Title||Semiparametric additive marginal regression models for multiple type recurrent events.|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Chen, Xiaolin, Qihua Wang, Jianwen Cai, and Viswanathan Shankar|
|Journal||Lifetime Data Anal|
|Date Published||2012 Oct|
|Keywords||Biomedical Research, Computer Simulation, Data Interpretation, Statistical, Humans, India, Infections, Kidney Transplantation, Models, Statistical, Recurrence, Regression Analysis|
Recurrent event data are often encountered in biomedical research, for example, recurrent infections or recurrent hospitalizations for patients after renal transplant. In many studies, there are more than one type of events of interest. Cai and Schaube (Lifetime Data Anal 10:121-138, 2004) advocated a proportional marginal rate model for multiple type recurrent event data. In this paper, we propose a general additive marginal rate regression model. Estimating equations approach is used to obtain the estimators of regression coefficients and baseline rate function. We prove the consistency and asymptotic normality of the proposed estimators. The finite sample properties of our estimators are demonstrated by simulations. The proposed methods are applied to the India renal transplant study to examine risk factors for bacterial, fungal and viral infections.
|Alternate Journal||Lifetime Data Anal|
|Original Publication||Semiparametric additive marginal regression models for multiple type recurrent events.|
|PubMed Central ID||PMC3844629|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 HL057444 / HL / NHLBI NIH HHS / United States
Semiparametric additive marginal regression models for multiple type recurrent events.