Lifetime Data Anal - Semiparametric additive marginal regression models for multiple type recurrent events.

Tópicos

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Resumo

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.

Resumo Limpo

recurr event data often encount biomed research exampl recurr infect recurr hospit patient renal transplant mani studi one type event interest cai schaub lifetim data anal advoc proport margin rate model multipl type recurr event data paper propos general addit margin rate regress model estim equat approach use obtain estim regress coeffici baselin rate function prove consist asymptot normal propos estim finit sampl properti estim demonstr simul propos method appli india renal transplant studi examin risk factor bacteri fungal viral infect

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