Sentence examples for multivariate modelling to from inspiring English sources

In the former case, we will use multivariate modelling to address potential confounding.

We applied multivariate modelling to predict methylation levels using an approach that selected a subset of explanatory variables, while catering for potential gender differences in each selected cofactor.

We used quantitative DNA methylation analysis combined with multivariate modelling to investigate the relationships between nutritional, anthropometric and metabolic factors and the CGI methylation of 11 genes, together with LINE-1 as an index of global DNA methylation.

Regression analyses were used to examine the degree of association between baseline vitamin D levels and demographic characteristics, fracture type, surgical procedure, and cognitive status - first at the univariate level, followed by multivariate modelling to determine those variables maintaining significance while controlling for all others.

Finally, we perform a multivariate model to identify variables associated with the occurrence of lymphopaenia, but we did not specifically investigate the lymphocyte count as a continuous variable.

We did not insert baseline assessments or the abovementioned covariates in the same multivariate model to avoid overfitting (Hawkins, 2004; Zhang, 2014).

Goodness-of-fit statistics indicate a limited ability of the multivariate models to predict admissions, suggesting that variables not included play a role.

Established prognostic variables were then added to the TIMI UA/NSTEMI multivariate model to evaluate their incremental benefit to model discrimination [11].

However, the variables sex and age were always maintained in the final multivariate models to account for possible remaining confounders.

A full multivariate model is therefore more accurate than its corresponding risk score, and refitting of an externally-derived multivariate model to the study population further improves model discrimination [21], [22].

As described in Methods, we fit a multivariate model to identify patient and ED characteristics that might predict adoption of at least a "basic" EMR.