Sentence examples for binomial regression modeling was from inspiring English sources

Negative binomial regression modeling was used to examine the association between numbers of emergency admissions from each practice in each year, and the practice level data on demographics, patient access and QoF [ 23].

The results from the negative binomial regression modeling are shown in Table  3 below.

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

Negative binomial regression model was found to be more suitable to identify the variables contributing to road crashes.

Diagnostic tests revealed that a negative binomial regression model was more appropriate than a regular Poisson model or zero-inflated negative binomial modelg.

A negative binomial regression model was fitted to the data using Stata statistical software.

Due to over-dispersed data for monthly malaria cases, a generalized linear negative binomial regression model was developed using the number of monthly malaria cases and climatic parameters.

A generalized linear negative binomial regression model was developed using the number of monthly malaria cases and each of the climatic parameters.

Since the likelihood ratio test for alpha = 0 (χ2(1) = 221.60; P = 0.000) indicated over dispersion, a robust negative binomial regression model was applied to predict the number of infections per woman (a count outcome).

Logistic regression model or binomial regression model was used to estimate the relative risk (RR) directly.

A negative binomial regression model was used to determine trends in prevalence.