Sentence examples for multivariate model selection from inspiring English sources
A multivariate model selection approach was implemented to test for effects due to statin use.
Multivariate model selection using a stepwise procedure based on the Akaike information criterion (AIC) was performed.
Stepwise multivariate model selection [ 28, 29] was used to determine the combination of covariates for the multivariate models.
A multivariate model selection approach was implemented to test for effects due to statin use, where we used a backward selection procedure to identify significant confounders.
The multivariate model selection was performed by a stepwise strategy using the likelihood ratio test to create a multiple risk factor model.
Variables that were significantly related to both the secondary sex ratio and PCB exposure terms (α ≤ 0.10) or that changed the odds ratio estimate for the main effect (≥ 10%), were included in multivariate model selection.
Briefly, these stages consist of (1) segmentation of soft tissue, (2) registration of the left images to the right images, (3) bilateral subtraction of the coregistered images, (4) filtering of the images to enhance the signal and texture, (5) feature extraction, (6) multivariate models selection using a train set, and (7) model evaluation on a test set.
The Cox multivariate model selected by Collett's Model Selection approach contained cognitive functioning, global health status and social functioning in addition to the selected clinical factors (Table 3).
In this study, we compared reaction norm and factor analytic models with the multivariate model without selection bias correction.
In each case, covariates that were associated in bivariate analyses at P < 0.20 were entered into an initial multivariate model; backward selection eliminated covariates that did not remain significant at P < 0.05 or did not change other coefficients by >10%.
Multivariate stepwise model selection identified ECOG status, neutrophil count, number of disease sites, time since initial diagnosis, and body mass index as significantly associated with OS (P≤0.05, Table 2).
