Sentence examples for binomial regression to adjust from inspiring English sources
After testing different models, we used negative binomial regression to adjust rural and urban cases.
Cancer incidence was modelled with both negative binomial regression to adjust for overdispersion and lack of fit in Poisson's regression.
For the full cohort, we examined the relationship between MH scores and mental health-related visits to a PCP or specialist using zero-inflated negative binomial (ZINB) regression to adjust for both over dispersion and the "excess" zeros in the data[ 28].
In order to detect any additive interaction between maternal socioeconomic status and SSRI-use on risk of congenital anomaly, we used Binomial Regression to calculate adjusted risk difference (RD) with associated 95% CIs.
We used binomial regression to calculate RRs adjusted for possible confounders.
We used negative binomial regression to estimate incidence rate ratios, adjusting for age, sex, and reporting period.
We compared incidence trends for maltreatment or violence-related (MVR) injury and adjusted rate differences between 2005 and 2011 using Poisson or negative binomial regression models to adjust for seasonal effects and secular trends in non-MVR injury.
We used a negative binomial regression to observe (1) the association of month of year with suicides, adjusting for different sunlight exposures, and (2) the association of sunlight exposure with suicides, adjusting for month of year.
Therefore, we used negative binomial regression to estimate the association of cleaning protocol (manual vs PPX-UV) with final colony count, adjusting for baseline counts.
We used negative binomial regression to estimate associations between birth defects and number of ALRI hospitalisations before age 2 years, adjusting for known risk factors.
4 7 20 21 We carried out multivariable log binomial regression to identify independent predictors of screening utilization within 24 months of the qualifying 2010 visit and to calculate adjusted screening rates.
