Sentence examples for binomial regression to describe from inspiring English sources
We used a negative binomial regression to describe the relationship of patient volume and whether a practitioner was solo or not.
As previously described (Wright et al. 2008), we used negative binomial regression to analyze arrest rates from 19 to 24 years of age among 250 members of the Cincinnati Lead Study.
We used binomial regression to calculate RRs adjusted for possible confounders.
After testing different models, we used negative binomial regression to adjust rural and urban cases.
We used logistic binomial regression to estimate relative risks (RR) and control potential confounders.
Logistic regression was used, instead of binomial regression, to avoid convergence issues when controlling for many confounding variables.
We used logistic regression and zero truncated negative binomial regression to model the zero and count processes, respectively.
We used log binomial regressions to control for any potential confounders.
Negative binomial regression was utilized to describe how functional status at hospital discharge differed with functional status at ICU admission, the extent of physical therapy received and hospital length of stay.
The second part uses the GEE negative binomial regression, described above, to estimate the number of mental health visits among veterans who had one or more mental health visits.
We consider the Poisson, negative binomial, and sCMP(m) models where m=1,2,3,4 to describe the data distribution; Bailey (1990) previously considered a binomial model to describe the data.
