Sentence examples for multivariate model factors from inspiring English sources

In the multivariate model, factors with p < 0.200 in the univariate analysis and important factors in the previous literature (4,8-11,14) were taken as covariates.

In the multivariate model, factors with p < 0.200 on univariate analysis and factors derived from the previous literature were considered covariates.

In the multivariate model, factors associated with consulting a GP were mobility problems (OR 2.62 (1.64-4.17)), urban living (OR 2.40 (1.14-5.04) and pain severity (1.28 (1.28-1.28)).

In the multivariate model, factors independently associated with mortality were increased age [adjusted hazard ratio (aHR), 1.03 per year; 95% confidence interval (CI), 1.00-1.05], cirrhosis (aHR, 3.01; 95% CI, 1.24-7.30), and ICU admission within 48 hours of diagnosis (aHR, 5.99; 95% CI, 2.86-12.58).

In the multivariate model, factors associated with unprotected sex in the 6 months following the assault included unprotected sex in the 6 months prior to the assault (OR = 6.46, 95% CI = 3.04, 13.74), time since the assault (per 30-day increments, OR = 1.33, 95% CI = 1.12, 1.57) and age (per 5-year increments OR = 1.30, 95% CI = 1.08, 1.57, Table  6).

In multivariate models, factors remaining included younger age, lower recent CD4 count, duration of antiretroviral treatment, and living in high TB burden countries.

In our multivariate models, factors that were independently associated with higher prevalence of underweight status included older age, less urbanization, current smoking status, and lower systolic blood pressure (Table 2).

In the multivariate models, factors independently and significantly associated with lower quality of care were age ≥75 years (prevalence rate ratio [PRR] 0.66 [95% CI 0.61 0.70]) and established cardiovascular disease (0.89 [0.89–0.89]).

The moderate discriminative value (AUC = 0.69) of the multivariate model describing factors associated with precautionary behaviour however suggests that other, unmeasured factors may be of additional importance.

We then included in a multivariate model all factors found to be significant at the 0.25 level and used backward elimination to remove factors until all remaining factors were significant at the 0.05 level.

Similar results were obtained in a secondary multivariate model analyzing factors associated with peak postoperative creatinine (variable estimate, -0.01; P = 0.04).