PAUC. Probit regression models were used to generate predicted probabilities of obesity for each ARIC participant using a baseline model that included demographic and geographic information and a test model that also included the GRS. AUCs and were calculated using these predicted probabilities as “risk scores” 36, and estimates of the differences between the baseline and test models were bootstrapped to obtain confidence intervals. AUC analyses were conducted using the Stata package “comproc” 37. 3) IDI, the integrated discrimination index for obesity. The IDI evaluates the added predictiveness of a marker by comparing predictions made using a baseline set of risk markers to predictions that also include information about the new risk marker: IDI=(Probtest,obese-Probtest,non-obese)-(Probbaseline,obese-Probbaseline,non-obese) where “Prob” is the average predicted probability for a particular group from a particular model. The IDI measures change in model sensitivity net of change in model specificity and is a more sensitive measure than delta AUC 38. An IDI of zero indicates that the test model performs comparably to the baseline model. Positive IDI values index net improvement in model sensitivity. Baseline and test models for IDI analyses were identical to those used in delta AUC analyses.
