Sentence examples for multivariable models yielded from inspiring English sources

Multivariable models yielded independent effects of ''witnessing violence'' (β=0.09, 95% CI: 0.01, 0.18) and ''Sexual abuse'' (β=0.09, 95% CI: 0.02, 0.19) on conduct problems while "duration in captivity" independently and negatively predicted conduct problems (β=−0.14, 95% CI: −0.23, −0.06).

Sex-, and-, and smoking-adjusted multivariable models yielded a non-significant increased odds of having advanced AMD among people carrying the T1-defining variants (OR = 4.40, 95%CI 0.69 27.96, 1-sided P≤0.058).

Multivariable models yielded similar results.

An additional sensitivity analysis performed using the door-to-balloon time cutpoints of ≤90 min and >90 min, treated as a confounding variable through inclusion in the multivariable models, yielded HRs that were essentially the same (30-day survival HR 1.23 (95% CI 0.77 to 1.99)).

Missing data of variables included in the multivariable model yielded to an exclusion of 23 women.

Substitution of sleep duration for trouble sleeping in multivariable modeling yielded nearly identical results, with significantly higher risk seen with <6 h of sleep.

Applying the multivariable logistic regression models yielded that various asthma phenotypes were positively associated with heavy parental smoking, traffic-related air pollution and dampness in the last 12 months and negatively associated with contact with cats and domestic wood heating in the last 12 months (see Additional file 1: Table S1).

In addition, multivariable-adjusted Cox regression models yielded estimated hazard ratios and 95% confidence intervals for the risk of dying at selected time points according to the number of cardiovascular comorbidities that had been previously diagnosed (a list of controlling variables is included in the footnotes of the tables).

The final multivariable GAMLSS model yielded the same significant covariates as the GLM methods using backward selection, with exception of breastfeeding for which the scale parameter σ was significant in the GAMLSS (tables 1 and 2).

In sensitivity analyses, each of the very large, excluded studies [ 16, 17] was included in turn in the final multivariable model; this yielded similar estimates of the effect of the included factors, but with more variation between studies.

Multivariable logistic regression model yielded an adequate fit (Hosmer-Lemeshow test chi-square = 11.3, P = 0.185).