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To allow for an adequate interpretation of the interactions effects in the models, we additionally calculate conditional marginal effects and marginal effect plots for continuous predictors (Royston 2013).
To assess the robustness of findings, we performed a sensitivity analysis by using restricted cubic splines for continuous predictors in the new model [37].
Values for continuous predictors are given in small steps.
Rather, they help to identify potentially useful predictors and potentially useful cutpoints for continuous predictors.
Several new statistical methods are available that allow nonlinear modelling for continuous predictors.
For continuous predictors, this assumption was checked using an extended Cox model with time-dependent covariates.
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For continuous predictor variables we conducted univariate meta-regression analyses as recommended by Higgins et al [26] and Thomson et al [28].
A method of fractional polynomials for continuous predictor variables will be used where appropriate.
aComparisons were made using chi-square analysis for dichotomous predictor variables and using an unpaired t test for continuous predictor variables.
Note: Full model's adjusted R2 =.25; F(6.29)= 16.92, p <.0001 Zero-order correlation is represented by Spearman's Rho for continuous predictor variables and point-biserial correlation for dichotomous predictor variables.
The associations between subject demographics and medical conditions and the presence of pre-diabetes were examined using the χ statistic for categorical predictor variables and logit modeling for continuous predictor variables.
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