Multivariable Poisson regression models with robust error estimates were used to estimate relative risk of outcomes.
Multivariable Poisson regression models with robust error estimates were used to estimate the relative risk of outcomes.
With over 10 years of experience developing hu-mouse models, we have pioneered novel humanized mouse models with robust, functional human immune systems.
Univariable and multivariable logistic regression models with robust standard errors adjusting for potential clustering of sites due to same ownership were computed.
Results from a series of multilevel logit models with robust standard errors reveal partial support for our hypotheses; specifically, we find that alcohol sales in a given neighborhood increase adolescents' alcohol use.
In general, similar results were obtained from a more parsimonious model for the clustering based on marginal models with robust standard errors (i.e., a generalized estimating equation framework).
Multilevel logistic models with robust variance will be used for binary outcome variables and mixed effect Cox model for survival data.
Once again, both full and reduced models with robust standard errors in parentheses are presented, and unlike the panel analysis, all of the variables remain significant.
Multivariate analysis through Poisson linear models with robust estimation were constructed to identify variables that are independently associated with an important quality-of-care marker: US-guided internal jugular vein (IJV) puncture, in compliance with international guidelines [13 15].
We used Poisson models with robust standard errors to assess the relationships between the mean and total CVL and the primary outcome: annual numbers of newly diagnosed HIV cases.
Hierarchical models with robust S.E.
