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For model building, we introduced selected variables from univariate analysis with p < 0.2.
Prior to model building, we tested for collinearity among explanatory variables using Spearman's rank correlation and included only variables with ρ < 0.7 in the same model (Dormann et al. 2013).
For model building we applied MODELLER [23,24].
For model building, we applied backward introduction of selected variables from univariate analysis (P-entry = 0.20).
For model building, we adopted default options including the radial kernel.
Because of the age stratification used in model building, we performed AUC analysis of 5-year risk outputs for women that fall into 5-year age intervals.
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Before model-building, we ruled out collinearity among main effects by Spearman's rank correlation coefficient with a cutoff of 10%.
In multivariable model-building, we pursued forward, backward, and the best subset selection strategies, as well as manual strategies.
Prior to model-building, we excluded 20% of the available examples from each data set in order to use them for independent validation.
2-week temperaverageserands and same-day temperatures both showed similar relatemperaturesth Sbothnd DBP in magnitude (all P <.0001); we ushowedme-day measimilarts in model building since we knew parelationshipsre at or near their homes during this day.
Using the change-in-estimate model building method, we identified depression, marital status, and income as confounders of this association.
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