A zero-inflated Poisson mixed model to analyze diagnosis related groups with majority of same-day hospital stays

Wang, Kui; Yau, Kelvin K. W.; Lee, Andy H.

doi:10.1016/s0169-2607(01)00171-7

Cited by 46 publications

(41 citation statements)

References 12 publications

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“…Wang et al fitted a zero-inflated Poisson (ZIP) mixed model to identify health and patient related characteristics associated with LOS and to model variations in LOS within Diagnosis Related Groups. Their investigation revealed that age, number of diagnoses, and admission types are important risk factors affecting LOS (20). In a study by Sepehri et al using the Vietnam National Health Survey and ZINB regression model, Vietnam's health insurance schemes has been identified as determinant of both hospital admission and LOS.…”

Section: Discussionmentioning

confidence: 99%

“…Rafeei and colleagues studied truncated numerical models in zero point with Poisson and negative binomial distributions as well as ZIP and ZINB regression models on the length of stay among mothers referred to health centers in Arak (7). In the case where the response variable is numeric and nonnegative, the proper model is Poisson or negative binomial regression (1,20,22). The assumptions of the conventional Poisson model constrain some count data (2,23).…”

Section: Discussionmentioning

confidence: 99%

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Efficiency of Zero-Inflated Generalized Poisson Regression Model on Hospital Length of Stay Using Real Data and Simulation Study

Hassankiadeh

Kazemnejad

Fesharaki

et al. 2018

CJHR

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Background: An important feature of Poisson distribution is the equality of mean and variance. However, additional zeroes in the data may cause over-dispersion in most cases, in which zero-inflated models are recommended. In this study, we aimed to evaluate the efficacy of zero-inflated models to predict hospital length of stay (LOS) using real data and simulated study. Methods: This study was conducted on patients admitted at Shariati hospital, Tehran, Iran. Zero inflated Poisson (ZIP), zero inflated negative binomials (ZINB) and zero inflated generalized Poisson (ZIGP) models were fitted on patient's length of stay. The fitted models were compared using the Akaike information criterion (AIC). The simulated data was generated using a model with the lowest AIC. Different models were then compared using the AIC. Data analysis was performed in R statistical software. Results:The results of both real data and simulation study showed lower AIC for ZIGP model compared to ZIP and ZINB model. Conclusion: Given the high dispersion and Zero Inflation in hospital LOS, the zero-inflated generalized Poisson regression model is the most suitable model to predict determinants of LOS. Keywords: Computer Simulation, Hospital, Length of Stay, Poisson Distribution Citation: Farhadi Hassankiadeh R, Kazemnejad A, Gholami Fesharaki M, Kargar Jahromi S. Efficiency of zero-inflated generalized poisson regression model on hospital length of stay using real data and simulation study.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Efficiency of Zero-Inflated Generalized Poisson Regression Model on Hospital Length of Stay Using Real Data and Simulation Study

Hassankiadeh

Kazemnejad

Fesharaki

et al. 2018

CJHR

View full text Add to dashboard Cite

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“…It may be assumed that the observations are independent between clusters but within-cluster correlation is anticipated. The ZIP mixed regression model assumes both ij and ij to be separate functions of some covariates and random e ects to adjust for the clustering [7]. Under this model framework,…”

Section: Zip Mixed Modelmentioning

confidence: 99%

“…With suitable initial values, the REML estimatesÿ andû (for conditionally ÿxed u) can be obtained by maximizing the log-likelihood l via a numerical procedure, such as the extended version of the EM algorithm for overdispersed count data [14,15]. The estimate of variance component 2 u is then computed from an estimating equation involvingÿ andû, details of which are described in Reference [7].…”

Section: Zip Mixed Modelmentioning

confidence: 99%

A score test for zero‐inflation in correlated count data

Xiang

Lee

Yau

et al. 2005

Statistics in Medicine

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SUMMARYTo account for the preponderance of zero counts and simultaneous correlation of observations, a class of zero-in ated Poisson mixed regression models is applicable for accommodating the within-cluster dependence. In this paper, a score test for zero-in ation is developed for assessing correlated count data with excess zeros. The sampling distribution and the power of the test statistic are evaluated by simulation studies. The results show that the test statistic performs satisfactorily under a wide range of conditions. The test procedure is further illustrated using a data set on recurrent urinary tract infections.

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“…To accommodate the inherent correlation, random effects u i and v i are incorporated in the linear predictors i j for the Poisson part and i j for the zero mixing part. The ZIP mixed regression model is thus [6]:…”

Section: Introductionmentioning

confidence: 99%

A score test for overdispersion in zero‐inflated poisson mixed regression model

Xiang

Lee

Yau

et al. 2006

Statistics in Medicine

Self Cite

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Count data with extra zeros are common in many medical applications. The zero-inflated Poisson (ZIP) regression model is useful to analyse such data. For hierarchical or correlated count data where the observations are either clustered or represent repeated outcomes from individual subjects, a class of ZIP mixed regression models may be appropriate. However, the ZIP parameter estimates can be severely biased if the non-zero counts are overdispersed in relation to the Poisson distribution. In this paper, a score test is proposed for testing the ZIP mixed regression model against the zero-inflated negative binomial alternative. Sampling distribution and power of the test statistic are evaluated by simulation studies. The results show that the test statistic performs satisfactorily under a wide range of conditions. The test procedure is applied to pancreas disorder length of stay that comprised mainly same-day separations and simultaneous prolonged hospitalizations.

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A zero-inflated Poisson mixed model to analyze diagnosis related groups with majority of same-day hospital stays

Cited by 46 publications

References 12 publications

Efficiency of Zero-Inflated Generalized Poisson Regression Model on Hospital Length of Stay Using Real Data and Simulation Study

Efficiency of Zero-Inflated Generalized Poisson Regression Model on Hospital Length of Stay Using Real Data and Simulation Study

A score test for zero‐inflation in correlated count data

A score test for overdispersion in zero‐inflated poisson mixed regression model

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