TY  - Type of reference 
TI  - Zero-inflated regression models for right-censored counts, with an application to healthcare utilization 
AU  - Van Trinh Nguyen 
AU  - Jean-François Dupuy 
AB  - Zero-inflated models for censored and overdispersed count data have received little attention so far, except for the zero-inflated Poisson (ZIP) model which assumes that overdispersion is entirely caused by zero-inflation. When additional overdispersion is present, useful alternatives to ZIP are given by the zero-inflated generalized Poisson (ZIGP) and zero-inflated negative binomial (ZINB) models. This paper investigates properties of the maximum likelihood estimator (MLE) in ZIGP and ZINB regression models when the count response is subject to right-censoring. Simulations are used to examine performance (bias, mean square error, coverage probabilities and standard error calculations) of the MLE. Results suggest that maximum likelihood yields accurate inference. A simple, efficient and easy-to-implement methodology for variable selection is also proposed. It is applicable even when the number of predictors is very large and yields interpretable and sound results. The proposed methods are applied to a dataset of healthcare demand. 
DO  - 10.21494/ISTE.OP.2019.0393 
JF  - Biostatistics and Health Sciences 
KW  - Excess of zeros,  maximum likelihood,  simulations, Excès de zéros,  maximum de vraisemblance,  simulations,  
L1  - https://www.openscience.fr/IMG/pdf/iste_biostat19v1n1_1.pdf 
LA  - en 
PB  - ISTE OpenScience 
DA  - 2019/05/29 
SN  - 2632-8291 
TT  - Modèles de régression à inflation de zéro et données censurées - application au recours aux soins de santé 
UR  - https://www.openscience.fr/Zero-inflated-regression-models-for-right-censored-counts-with-an-application 
IS  - Issue 1 
VL  - 1 
ER  -