Sentence examples for binomial regression analysis was from inspiring English sources
A binomial regression analysis was run to test the effect of rizatriptan 10 mg versus placebo, and the therapeutic gain (defined as the change in outcome induced by rizatriptan 10 mg minus the placebo effect) with its relative 95%% confidence interval (CI), was estimated for all outcome measures.
Binomial regression analysis was performed across the entire study group.
Individual level bivariate negative binomial regression analysis was performed to explore the associations between clinical malaria episodes and age and sex.
A negative binomial regression analysis was used to determine longitudinal usage trends and compare prevalence rates across years, sex and age groups.
A negative binomial regression analysis was used to investigate trends and compare rates across years, sex and age groups (0 4, 5 11, 12 15 years).
Log binomial regression analysis was used to calculate relative risks (chance) on compliance, stratified by medical specialty, calendar time and participation period.
All the independent variables entered in the log binomial regression analysis were significantly associated with the dependent variable (managing occupational exposure to HIV) at bivariate analysis.
Log-binomial regression analysis was used to determine the risk ratio (RR) of recurrent adenomas, adjusted for covariates and an interaction term for obesity and treatment.
Uni- and multivariate log-binomial regression analysis was conducted to determine the prevalence ratios for the outcome, CKD [ 26, 27], using the following dichotomized variables: age (≥ 70 vs. < 70), sex (male vs. female), diabetes (yes/no), hypertension (yes/no), CV disease (yes/no), and department of admission (medical vs. surgical).
Binomial logistic regression analysis was used to identify the predictors of having considered the impact of, having a plan for, and needing help to prepare for pandemic influenza.
Binomial logistic regression analysis was used to analyse to what extent factors related to socialisation and family circumstances can predict the probability of being a lowering-effect family as compared to a raising-effect family (reference group).
