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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).
Variables considered for the multivariate model, selected as significant in the univariate model, were CTC count, number of metastatic sites, molecular subtype, and metastatic site.
The multivariate model selected three independent features associated with the development of HCC in this cohort, namely older age, male gender, and high Child-Pugh score.
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In a randomly selected cohort (n = 599), a multivariate ordinal regression model selected resting heart rate, plasma glucose and hypertension as significant independent predictors for presence of albuminuria at any degree (table 3).
The final multivariate logistic regression model selected by a stepwise procedure included only one independent variable, the CRP that was the only independent factor associated with mixed etiology.
Parameters included in the multivariate model were selected according to the statistical significance in univariate models.
The variables introduced into the multivariate model were selected considering the clinical context.
Variables for the multivariate model were selected according to the recommendations of Hosmer and Lemeshow (28 ).
The variables introduced into the multivariate model were selected based on the bivariate analysis and published data.
Variables for each multivariate model were selected using a p = 0.25 cut point and were retained in the final model if the covariate achieved statistical significance p < 0.05 (38).
A multivariate model from selected known clinical prognostic factors such as tumor size, grade, LN status, LVI and age was created.
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CEO of Professional Science Editing for Scientists @ prosciediting.com