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The score was calculated using logistic regression with backward inclusion of the covariates.
The mean BMI of the patients was a treatment-dependent factor that was not associated with the T2DM-related event risk in a statistically significant way; so it was excluded based on our backward inclusion methodology.
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Univariate logistic regression analysis, employing a backward stepwise inclusion method, was developed using a P-value of 0.05 to discriminate which variables affected the outcome.
Both forward and backward procedures for inclusion of variables in the models were used sequentially.
%: the proportion of times that the indicator is retained in the backward regression models (inclusion frequency).
Multiple binary logistic regression including risk factors with p < 0.40 in a bivariable model, forward selection, and backward elimination methods (inclusion criteria, p < 0.20) were used to investigate the potential confounding effect of other risk variables.
[ 12, 41] The minimum weight assigned was 0; the maximum assigned weight was 2. Individual Weighted risk scores were computed by summarising the separate score See Table 5.> > > > -wrap-foot> Results from logistic regression analysis with forward and backward approach (P inclusion = P exclusion = 0.10) for all 5 imputed datasets separately.
This clarification includes a specification of the variables, defined in item 6, that were initially considered and which procedures were followed for a final selection (e.g., backward deletion or forward inclusion, and the criteria for deletion and inclusion), if applicable.
Subsequently, selected covariate relationships were evaluated by forward inclusion and backward deletion procedure.
A significance level of 0.01 was used for both forward inclusion and backward elimination steps.
Finally, after forward inclusion, a backward exclusion procedure was applied to justify the covariate.
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