Sentence examples for multivariable models after from inspiring English sources

In other attempted multivariable models, after adjusting for baseline serum chromogranin A concentration and age, neither clinical or biochemical response group were found to affect survival (not shown).

The associations between VAT and inflammatory markers were further examined by multivariable models after addition of SAT, or of hip circumference, to models that included BMI.

Variables associated with the outcomes at a level of p < .10 in unadjusted analyses were entered into the final multivariable models after assessing for the presence of multicollinearity.

The scales were examined in the multivariable models after collapsing the scores into 3 levels: high score (score > interquartile range), mid-range score (within interquartile range) and low score (score < interquartile range).

Thus, in the multivariable models, after adjusting for donor type and conditioning regimen we observed that every 50 ng/mL increase in the weighted average trough CsA level resulted in a 33% reduction in the risk of grade 2 4 (Table 2).

While several co-variates met the pre-specified p value cut-off for entry into the multivariable model, after backwards elimination of non-significant predictors, only adherence remained.

While several co-variates met the pre-specified p value cut-off for entry into the multivariable model, after backwards elimination of non-significant predictors, only exercise time remained.

In the multivariable model, after adjusting for sociodemographic, behavioural and psychosocial factors, only two factors remained statistically significant: being married to a woman and sexual stigma.

To describe the combined epidemic outside the known high-risk groups, we ran the final multivariable model after excluding all IDUs.

Variables shown to be associated with postnatal transmission on the basis of a P value <.2 in univariate analysis were included in the multivariable model, after verifying the absence of multicollinearity.

The new marker is accepted as having predictive value if it is significantly associated with outcome in the multivariable model, after adjusting for established markers; if so, the marker is commonly referred to being an "independent" predictor of outcome.