A flexible regression model for zero- and k-inflated count data

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

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A multivariate Poisson regression model for count data

Journal of Applied Statistics ◽

10.1080/02664763.2021.1877637 ◽

2021 ◽

pp. 1-17

Author(s):

J. M. Muñoz-Pichardo ◽

R. Pino-Mejías ◽

J. García-Heras ◽

F. Ruiz-Muñoz ◽

M. Luz González-Regalado

Keyword(s):

Regression Model ◽

Count Data ◽

Poisson Regression ◽

Poisson Regression Model

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PARAMETER ESTIMATION ON HURDLE POISSON REGRESSION MODEL WITH CENSORED DATA

Jurnal Teknologi ◽

10.11113/jt.v57.1533 ◽

2012 ◽

Vol 57 (1) ◽

Author(s):

SEYED EHSAN SAFFAR ◽

ROBIAH ADNAN ◽

WILLIAM GREENE

Keyword(s):

Regression Model ◽

Count Data ◽

Poisson Regression ◽

Goodness Of Fit ◽

Poisson Model ◽

Likelihood Method ◽

Poisson Regression Model ◽

Response Variable ◽

The Mean ◽

Over Dispersion

A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the same as before. In fact, the variance value of the dependent variable will be much more than the mean value of the dependent variable and this is called over–dispersion. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, it is suggested to use a hurdle Poisson regression model to overcome over–dispersion problem. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle Poisson regression model is introduced on count data with many zeros. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness–of–fit for the regression model is examined. We study the effects of right censoring on estimated parameters and their standard errors via an example.

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A Combined PLS and Negative Binomial Regression Model for Inferring Association Networks from Next-Generation Sequencing Count Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2017.2665495 ◽

2018 ◽

Vol 15 (3) ◽

pp. 760-773 ◽

Cited By ~ 2

Author(s):

Maiju Pesonen ◽

Jaakko Nevalainen ◽

Steven Potter ◽

Somnath Datta ◽

Susmita Datta

Keyword(s):

Next Generation Sequencing ◽

Regression Model ◽

Count Data ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Negative Binomial Regression Model ◽

Next Generation ◽

Binomial Regression ◽

Generation Sequencing

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A Bayesian latent process spatiotemporal regression model for areal count data

Spatial and Spatio-temporal Epidemiology ◽

10.1016/j.sste.2018.01.003 ◽

2018 ◽

Vol 25 ◽

pp. 25-37 ◽

Cited By ~ 5

Author(s):

C. Edson Utazi ◽

Emmanuel O. Afuecheta ◽

C. Christopher Nnanatu

Keyword(s):

Regression Model ◽

Count Data ◽

Latent Process

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PEMODELAN DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (Studi kasus: Banyaknya Penderita Kusta di Jawa Barat)

Xplore Journal of Statistics ◽

10.29244/xplore.v10i3.833 ◽

2021 ◽

Vol 10 (3) ◽

pp. 226-236

Author(s):

Khusnul Khotimah ◽

Itasia Dina Sulvianti ◽

Pika Silvianti

Keyword(s):

Regression Model ◽

Count Data ◽

Poisson Regression ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Kernel Weight ◽

Negative Binomial Regression Model ◽

West Java ◽

Binomial Regression ◽

Spatial Heterogenity

The number of leper in West Java is an example of the count data case. The analyzes commonly used in count data is Poisson regression. This research will determine the variables that influence the number of leper in West Java. The data used is the number of leper in West Java in 2019. This data has an overdispersion condition and spatial heterogenity. To handle overdispersion, the negative binomial regression model can be employed. While spatial heterogenity is overcome by adding adaptive bisquare kernel weight. This research resulted Geographically Weighted Negative Binomial Regression (GWNBR) with a weighting adaptive bisquare kernel classifies regency/city in West Java into ten groups based on the variables that sigfinicantly influence the number of leper. In general, the variable in the percentage of households with Clean and Healthy Behavior (PHBS) has a significant effect in all regency/city in West Java. Especially for Bogor Regency, Depok City, Bogor City, and Pangandaran Regency, the variable of the percentage of people poverty does not have a significant effect on the number leper.

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