Negative binomial regression

Abstract

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Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases

“…Main effects of covariates (age, sex, household size, and country) on numbers of contacts were assessed using multiple censored negative binomial regression [27]. The data were right censored at 29 contacts for all countries because of a limited number of possible diary entries in some countries.…”

Schistosomiasis in Lake Malaŵi Villages

“…Similarly, egg counts were compared among these factors using count models (Hilbe, 2008) adjusting for clustering within villages. In these analyses, we first used Poisson regression which involves estimation of one parameter, the mean (l).…”

“…If the Poisson modelled analysis showed real overdispersion and the variance (V) was greater than l, we would try to model using negative binomial regression where the variance is modelled as V = l + a 9 l 2 . The ancillary parameter (a) was estimated using full maximum likelihood estimation as described in Hilbe (2008). The ancillary parameter was then entered into a Generalized Linear Model and model fit was assessed using dispersion statistics to check for overdispersion and Anscombe residuals to check for outliers (see Hilbe, 2008).…”

“…The ancillary parameter (a) was estimated using full maximum likelihood estimation as described in Hilbe (2008). The ancillary parameter was then entered into a Generalized Linear Model and model fit was assessed using dispersion statistics to check for overdispersion and Anscombe residuals to check for outliers (see Hilbe, 2008). The volume of urine examined (usually 10 ml) was used as an offset.…”

The COM‐Poisson model for count data: a survey of methods and applications

“…The most common model is Poisson regression, yet it is limiting in its equidispersion assumption. Much attention has focused on the case of data over-dispersion (e.g., [23,24], and [5]), which arises in practice because of experimental design issues and/or variability within groups. Many of the proposed approaches (e.g., [25]), however, cannot be applied to address under-dispersion and/or have restrictions that make such approaches unfavorable [4].…”