Sentence examples for regression of potential from inspiring English sources

Unfortunately, their multiple regression of potential predictive variables cannot be interpreted reliably.

Main outcome measure Odds ratios for being a "case," with multivariable conditional logistic regression of potential risk factors including pre-admission characteristics and progress during the course.

Regression models of potential predictor variables were created for the 3 self-reported xerostomia measures and 4 salivary flow rate variables.

Logistic regression analysis of potential prognostic factors for the presence of spread lesion or recurrence over 2 years showed that 4DST was the most influential factor (odds ratio 29.08), but it was not significant (P = 0.05).

The results of the Cox proportional hazards regression analysis of potential factors affecting OS are shown in Table  2.

Table 4 summarizes results from univariate and multivariate regression analyses of potential lncRNAs associated with HCC disease-free survival.

Linear regression analyses of potential predictors of the change in overall performance in total UMAT percentile score and each of its sub-sections on re-testing are outlined in Tables  1, 2, 3 and 4. Performance in UMAT-1, UMAT-2 and UMAT-3 were first forced into the model to correct for regression to the mean.

Variables from univariate analysis (with P < 0.05) were included into a model of multivariable analysis with logistic regression for identification of potential predictor variables of a final diagnosis of PE.

We review key information of selected models, including etiology, pathogenesis, procedure, time course and assessment of potential regression.

We stratified patients 16 55 years of age into four age groups for comparison and used multivariable logistic regression to identify associations of potential susceptibilities and exposures with AKI and mortality.

A multivariate model for risk of tinnitus was estimated by means of logistic regression using stepwise inclusion/exclusion of potential predictor variables (pin = 0.05, pout = 0.10) as model building strategy.