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Confounding was evaluated by entering potential confounders into a logistic regression model sequentially and comparing adjusted and unadjusted ORs.
The effect of baseline differences in catastrophising was evaluated by entering this variable into the multilevel analysis models.
Nonlinearity was evaluated by entering higher order polynomial terms for temperature in the model and conducting likelihood ratio tests.
Tests for trend with two-sided P-values were evaluated by entering the categorical terms as an ordinal variable in the model.
The potential for predictor variables to interact was evaluated by entering each of the potential 28 interaction terms in turn to the logistic regression.
Alcoholic beverage types were also evaluated by entering beer, wine, and liquor consumption into a model with or without adjusting for other beverage type, with nondrinkers of a specific beverage type as the reference category.
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Results were confirmed by examining outputs from backwards and forwards stepwise regression analyses as evaluated by the probability of F (enter p ≤ 0.05, remove p ≥ 0.10).
When all these lncRNAs were evaluated by a multivariate model using enter selection, HULC was indicated to be a positive factor for HCC overall survival (HR = 0.885, 95% CI = 0.797 0.983, and P = 0.023).
Predictors of improvement in systolic function and non-response to CRT were evaluated by univariate tests and were entered into a multivariable analysis (backward stepwise regression) if showing significant univariate correlation.
We therefore retained first-order terms in subscale-based regressions solely based on their contribution to the regression as evaluated by the probability of F (enter p ≤ 0.05, remove p ≥ 0.10).
In the case of item-based algorithms, we retained first-order terms in the item-based model solely on the basis of their contribution to the regression; as evaluated by the probability of F (enter p ≤ 0.05, remove p ≥ 0.10).
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