Figure 4.

Development of ICD behavior classifier model. (A) The Bayesian logistic regression model estimates of the effects of two SNPs, adjusted for cohort, age at test, sex, dopamine agonist use, levodopa use, disease duration and ethnicity. We calculated the upper (UCL) and lower (LCL) confidence limits of odds of ICD behavior as: CL = odds ±1.96 SE (odds), where $\text{odds}={\exp }^{\mathit{\beta x}}$, and $\mathit{\beta x}$ is a linear predictor of ICD. Cohort = UPenn versus PPMI, with UPenn associated with higher risk of ICD (positive estimate), Sex = female versus male, with females associated with lower risk of ICD (negative estimate). (B) The performance of the Bayesian classifier model measured in the Training dataset (261 ICD− and 78 ICD+ participants) by ROC‐AUC was 75%. The same model achieved ROC‐AUC = 72% when we performed prediction in the non‐overlapping Test dataset (130 ICD− and 39 ICD+ participants). (C) Estimating the best ROC‐AUC cutoff point in the Test dataset. Specificity and sensitivity of final Bayesian logistic regression model when predicting ICD behavior in the Test dataset across a range of cutoff points. We performed this analysis using the method closest.topleft (pROC package function coords), which revealed 0.23 as the best cutoff point, yielding an accuracy of 70%, sensitivity of 69% and specificity of 72% (dotted lines). ICD, impulse control disorder; ROC‐AUC, receiver operator characteristic curves‐area under the curve. *p < 0.05, **p < 0.01, ***p < 0.001. [Colour figure can be viewed at wileyonlinelibrary.com]