Evaluation of negative binomial and zero-inflated negative binomial models for the analysis of zero-inflated count data: application to the telemedicine for children with medical complexity trial

[1] Yang Z(2010)Score tests for zero-inflation in overdispersed count data Commun Stat Methods 39 2008-2030

[2] Hardin JW(2021)A zero inflated log-normal model for inference of sparse microbial association networks PLOS Comput Biol 17 e1009089-304

[3] Addy CL(2003)Estimating the size of a criminal population from police records using the truncated Poisson regression model Stat Neerlandica 57 289-793

[4] Prost V(2013)Negative binomial additive models for short-term traffic flow forecasting in urban areas IEEE Trans Intell Transp Syst 15 784-2772

[5] Gazut S(2007)Maximum likelihood estimation of the negative binomial dispersion parameter for highly overdispersed data, with applications to infectious diseases PLoS ONE 2 e180-14

[6] Brüls T(2007)Quasi‐Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology 88 2766-365

[7] Van Der Heijden PG(1992)Zero-inflated Poisson regression, with an application to defects in manufacturing Technometrics 34 1-44

[8] Cruyff M(1986)Specification and testing of some modified count data models J Econom 33 341-340

[9] Van Houwelingen HC(2012)The use of count data models in biomedical informatics evaluation research J Am Med Inform Assoc 19 39-e77

[10] Daraghmi Y-A(2012)The impact of electronic health records on care of heart failure patients in the emergency room J Am Med Inform Assoc 19 334-16

[1] Yang Z(2010)Score tests for zero-inflation in overdispersed count data Commun Stat Methods 39 2008-2030

[2] Hardin JW(2021)A zero inflated log-normal model for inference of sparse microbial association networks PLOS Comput Biol 17 e1009089-304

[3] Addy CL(2003)Estimating the size of a criminal population from police records using the truncated Poisson regression model Stat Neerlandica 57 289-793

[4] Prost V(2013)Negative binomial additive models for short-term traffic flow forecasting in urban areas IEEE Trans Intell Transp Syst 15 784-2772

[5] Gazut S(2007)Maximum likelihood estimation of the negative binomial dispersion parameter for highly overdispersed data, with applications to infectious diseases PLoS ONE 2 e180-14

[6] Brüls T(2007)Quasi‐Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology 88 2766-365

[7] Van Der Heijden PG(1992)Zero-inflated Poisson regression, with an application to defects in manufacturing Technometrics 34 1-44

[8] Cruyff M(1986)Specification and testing of some modified count data models J Econom 33 341-340

[9] Van Houwelingen HC(2012)The use of count data models in biomedical informatics evaluation research J Am Med Inform Assoc 19 39-e77

[10] Daraghmi Y-A(2012)The impact of electronic health records on care of heart failure patients in the emergency room J Am Med Inform Assoc 19 334-16