Sentence examples for multivariate models using from inspiring English sources

Predictive variables with some association with vaccination (p<0.05) were tested in multivariate models using forward stepwise multivariable logistic regression and the likelihood ratio method.

The methods were based on multivariate models using either (1) fatty acid composition monitored by GC-FID or (2) fluorescence landscape signals analysed using the PARAFAC model and in both cases predicting dioxin content as pg PCDD/F TEQ-WHO/g lipid.

Univariate and separate multivariate models using pre-cryo and post-thaw datasets including clinical parameters identified predictors of engraftment and survival.

In both methods, we first calculated crude odds ratio (OR) and hazard ratio (HR) with their 95% confidence intervals (CI) and then adjusted them with multivariate models using a backward selection process with p <0.1 used as the cutoff for retention in the models.

We report results from the multivariate models using odds ratios, OR (95% confidence intervals, 95% CI).

All non-binary variables were added to multivariate models using indicator variables.

Multivariate models used the backward selection technique and an alpha of 0.50 [18].

Multivariate models used multiple logistic regression.

The multivariate models used in this study were similar but not identical with equations used in risk engines 7 and risk scores.

Residual diagnostics were performed for the multilevel multivariate models used in the analysis via plots of residual values for both the fixed and random effects.

All differences in baseline characteristics between the cohorts were accounted for in the multivariate models used to compare adherence and persistence as measured by the PDC.