Sas 9.2 proc

Risk scores based on the logistic models were generated in Survey 1 using the WMH coefficients and the Survey 1 predictors. This direct estimation method could not be used for the ML models, though, as Survey 1 did not assess a number of significant predictors in the ML models (symptoms of anxious-depression and mixed episodes in incident episodes, comorbid obsessive-compulsive disorder, intermittent explosive disorder, and oppositional-defiant disorder). We addressed this problem by imputing ML risk scores to Survey 1 respondents from a consolidated dataset that combined WMH respondents and Surveys 1–2 respondents. The dataset included all predictors in common across the surveys along with the 4 ML predicted risk scores. The latter 4 scores had valid values for WMH cases and missing values for Survey 1 cases. Multiple imputation was applied to this dataset to generate 10 predicted scores on each missing variable to each Survey 1 respondent using SAS 9.2 proc mi.²⁵ Modal imputed values were assigned to each Survey 1 respondent for purposes of analysis. As these scores were strongly correlated across outcomes, a single composite ML predicted risk score was then constructed for each respondent by averaging across the four scores after transforming to percentiles.