Sentence examples for binomial regression models were from inspiring English sources

To examine the association between race, ethnicity sex, insurance, age, and age group with CRS, univariable and multivariable log binomial regression models were used.

Negative Binomial regression models were fitted to the incident number of infections with an offset with the annual number of ICU stays.

Negative binomial regression models were constructed to test associations between generic E. coli (MPN/100 mL) in water from farms and variables.

Negative binomial regression models were used to assess the effect of street and street network characteristics on total crashes, severe injury crashes, and fatal crashes.

Negative binomial regression models were used to assess the statistical significance of risk factors associated with the within-farm number of Campylobacter-positive flocks (the outcome).

The randomized quantile residuals of the finally selected model (Eq. 8.51 Duchon) using 1st order derivatives in the penalty and three other negative binomial regression models were compared and validated for spatial independency.

As with state-level analyses, negative-binomial regression models were repeated in the same pattern as primary analyses; models were fit using PROC GENMOD in SAS.

Log-binomial regression models were used to calculate prevalence ratios with 95% confidence intervals.

Logarithmic-binomial regression models were used to determine relative risks (RRs) and 95% CIs for SGA.

Negative-binomial regression models were estimated using generalized estimating equations assuming an autoregressive covariance structure.

Multiple log-binomial regression models were used, as the outcome was binary; procedure PROC GENMOD was applied for model adjustment.