Sentence examples for multivariate analysis selection from inspiring English sources

Basic statistical analysis was conducted to inform the multivariate analysis selection process.

The multivariate analysis selection criterion from the univariate analysis was P value < 0.05 and absence of collinearity.

In multivariate analysis, the selection of independent variables was initially determined through literature and theoretical concepts, including socio-demographic characteristics, population types, stigma and discrimination, CD4 testing, relevant risk factors, and exposure to the intervention.

On multivariate analysis, backward selection of variables yielded two that remained significantly associated with IgM seropositivity: absence of air-conditioning (odds ratio [OR] 2.6; CI 1.2 to 5.6) and no history of travel beyond Laredo/Nuevo Laredo in the previous 3 months (OR 2.0; CI 1.0 to 4.0).

We used multivariate analysis and variable selection to select significant factors associated with survival of ESCC patients.

Co-variates were entered into the multivariate analysis using backward selection.

In multivariate analysis, a forward selection approach was then used to include variables from the model based on a likelihood ratio test.

A multivariate analysis with forward selection was used to avoid multicollinearity and to find a good model for describing the observed number of HFRS cases.

A multivariate analysis using feature selection and supervised machine learning leads to a reasonable classification performance with a weighted F-score of 83.7% and an accuracy of 83.9%.

Cox proportional hazards regression model was used to estimate the risk of death by multivariate analysis (backward stepwise selection method with probability for the removal of 0.10) for the whole population.

Furthermore, multivariate analysis using forward selection has shown that HULC should play a positive role in prolonging HCC disease-free survival time (HR = 0.913, 95% CI = 0.835 0.998, and P = 0.045).