Paul R. Yarnold & Ariel Linden
Optimal Data Analysis, LLC
A maximum-accuracy machine-learning method for predicting dose of exposure based on distribution of the response variable was recently introduced. Herein we demonstrate the advantages of eliminating baseline variation in the response variable via transformation by ipsative standardization. Using data measuring forearm blood flow responses to intra-arterial administration of Isoproterenol, findings obtained using optimized discriminant analysis and a general estimating equation are compared separately for black and white males (and pooled data) using raw versus ipsatively standardized blood flow data. Findings using raw versus ipsatively standardized forearm blood-flow response data were incongruous. The standardized responses of blacks and whites were indistinguishable through 150 ng/min doses; responses of blacks were elevated at 300 ng/min dose, but at 400 ng/min dose responses regressed to 150 ng/min-dose-levels; while at 400 ng/min dose whites had the greatest response levels observed in the study. Using raw data there was no evidence of inter-method statistical conclusion agreement; baseline variability resulted in failure to statistically confirm numerous inter-dose responses; and the dose-response model yielded moderate predictive accuracy. Using standardized data there was significant evidence of inter-method statistical conclusion agreement; eliminating baseline variability yielded more findings of statistically reliable inter-dose responses; and the dose-response model yielded relatively strong predictive accuracy. This study adds to a growing literature demonstrating that ipsative standardization of the response variable studied in single-case or multiple-observation “repeated measures” designs yields generalizable models that generate the most accurate predictions (normed against chance) that are analytically possible for the sample data.