We used linear mixed-effects models to assess longitudinal variation in fatigue scores and generalized estimating equations for binary outcomes to model predictors of fatigue remission among those fatigued at baseline.
Logistic regression was used to model predictors of each unintentional non-adherence behavior.
A multivariate analysis of covariance model with backwards elimination was used to model predictors of beliefs about medicine.
Finally, we modelled predictors of length of hospital stay for the majority of these terminally ill hospice patients who survived to discharge.
Using a retrospective cohort study design, we modeled predictors of transfusion events within 24 hours of hospital admission and throughout the entire hospitalization.
Those characteristics where there was a statistically significant (p<0.05) or borderline significant difference between people with low and adequate health literacy were entered into the multivariable model; logistic regression was used to model predictors of low health literacy.
The aim of this study was to model predictors of recall that could be used in clinical practise to help reduce the numbers of repeat examinations required due to blurring.
We used multivariate logistic regression to model predictors of being a structured documenter, defined as using electronic templates or prepopulated dot phrases to document at least two of the three note sections (history, physical, assessment and plan).
In logistic regression models, predictors of opioids administration included male patient gender (OR = 0.58), male patient-physician interaction (OR = 2.58), arrival pain score (OR = 1.28), average pain score (OR = 1.10), and number of pain assessments (OR = 1.5).
In logistic regression models, predictors of ED analgesic administration were male physician (odds ratio [OR] = 0.7), arrival pain (OR = 1.3), number of pain assessments (OR = 1.83), and charted follow-up plans (OR = 2.16).
