Significats de negative binomial en anglès

Logistic and negative binomial regression models were used for the multivariate analysis.

For number of hospitalizations in the first year, we used negative binomial regression.

This will be analyzed by negative binomial regression, yielding density ratios.

These include the Poisson, over-dispersed Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial.

Microbial taxa were identified using zero-inflated negative binomial modeling.

We assessed socio-economic differences in the incidence and prevalence of leg ulcers using negative binomial regression.

Incidence rates were compared with an incidence rate ratio (IRR) generated by negative binomial regression.

The mean number of infants' contacts and factors associated with contact numbers were assessed using negative binomial regression.

The study demonstrates that the GNMs show slightly better model fitness than negative binomial (NB) models.

The negative binomial and the zero inflated Poisson and negative binomial models fitted the data better than Poisson.

Poisson and negative binomial regression models were fit to estimate the association of smoking status with hospitalization rate.

Multivariable negative binomial models were fitted to examine the association between the exposures and the adverse birth outcomes.

Using negative binomial regression, we determined characteristics associated with the number of secondary cases resulting from each infected individual.

Methods: We quantified heterogeneity in secondary infections per infectious individual by describing this number as a negative binomial distribution.

A zero-inflated negative binomial mixed effects model was used to evaluate geographical and climatological predictors of I. angustus burden.

A negative binomial regression model adjusting for inter-hospital variations was used to determine the prognostic factors influencing recurrent gastroenteritis.