Sentence examples for binomial regression modelling from inspiring English sources

Data were analysed by using negative binomial regression modelling.

Comparisons will comprise logistic regression models for binary outcomes and negative binomial regression modelling for count variables, adjusting for potential confounders.

Table  2 shows the results of the negative binomial regression modelling to assess the patterning of green space availability by neighbourhood socioeconomic circumstance, controlling for city and population density.

In order to compare the reported typical consumption of beer, wine and spirits across months of the year, we employed negative binomial regression modelling (NBR) for over-dispersed variables and many data points at zero [ 33, 34].

The impact of referral on SPPCS was explored using negative binomial regression modelling (which was chosen over Poisson regression due to overdispersion), with the total number of hospital admissions as the main outcome.

Differences in intervention rate were assessed using negative binomial regression modeling.

First, an unadjusted log binomial regression model was performed by regressing the prevalence of metabolic syndrome on prior service.

We use survey data and employ negative binomial regression models.

Event rate ratios were estimated using a multivariable negative binomial regression model.

In the empirical analysis, we use panel data and negative binomial regression models with fixed effects.

Negative binomial regression models are then used to define the role of risk factors and the diagnosing physician type.