Sentence examples for multivariate model adjusting from inspiring English sources

The multivariate model adjusting for potential confounding factors also included sex (male/female), age (10 13 years/14 18 years), as well as Saudi Arabian nationality (yes/no) as dichotomous variables.

A multivariate model adjusting for age, sex and study centre confirmed these general trends in prevalence (Figure 1).

Footnote= 1.00 reference category for logistic regression analyses, # multivariate model adjusting for all variables.

A multivariate model adjusting for age and gender of patients was also performed for selected genes and isoforms.

The comparisons were significant (p < 0.001) in a linear mixed multivariate model adjusting for a number of confounding factors.

Statistically significant (p = 0.05) variables and important explanatory variables were considered for the final multivariate model adjusting for age and province of residence.

However, of these factors, only documented restraints (B = −0.232), reported pain (B = −0.251) and NPI-NH score (B = −0.158) remained independently associated with self rated QoL-AD in the final parsimonious multivariate model (adjusted R2 = 0.128).

This surprisingly negative association was retained (P = 0.017) under the multivariate model adjusted for BMI, age and gender.

†: Multivariate model adjusted for age, sex, peptic ulcer history, ulcer bleeding history, Helicobacter pylori eradication rate, and comorbidities.

‡: Multivariate model adjusted for age, sex, peptic ulcer history, ulcer bleeding history, Helicobacter pylori eradication rate, comorbidities, and medications.

Also, although our final multivariate model adjusted for all covariates, it remains possible that additional unaccounted factors explain our findings.