Sentence examples for multivariable models where from inspiring English sources

Age, BMI and smoking were considered as confounders or covariates of importance, and adjusted for in multivariable models where appropriate.

Finally, for exposure biomarkers with multiple significant predictors, to explore confounding we constructed multivariable models where these predictors were included simultaneously.

The data were analysed by use of two multivariable models where the response was the area of the shoulder ulcer on day 14 and day 21, respectively.

As was discussed in the companion paper, drevers had a high rate of death due to traumatic causes, and that was reflected in the multivariable models, where they were at 4.5 times increased risk than the baseline group.

Interactions between predictor variables and between predictor variables and follow-up were tested for and included in the multivariable model where relevant.

This difference remained significant in the multivariable model where cows with isolates from the less common/rare cluster group had significantly higher SCC (p = 0.009) compared to cows with isolates from the common clusters.

Obesity and ethnic group were considered the main explanatory factors and were included in all multivariable models, other variables were included in multivariable modelling where there was evidence that they were independently associated with stillbirth (p < 0.05 for at least one parameter using the Wald test after adjustment for other variables).

The only multivariable model where more than one explanatory variable was significantly associated with the analysed uropathogen was the model of association with findings of beta haemolytic Streptococcus spp. Hence, the results for all other uropathogens are from the univariable logistic regression analyses.

Next, we constructed our "core" multivariable regression models where the outcome was phthalate metabolite concentrations and the independent variables were NHANES sampling cycle and urinary creatinine concentrations (to account for urinary dilution) (Barr et al. 2005).

These were put into the same two-HapMap SNP multivariable models described above where possible in order to evaluate the sensitivity of the data.

In order to assess the shape of association between the later two iron status biomarkers and event-free survival rates, univariable and multivariable models were constructed, where these variables were included as 1) log transformed and 2) transformed using natural cubic splines with n knots (23).