Regression-based formulas for predicting change in RBANS subtests with older adults

Repeated neuropsychological assessments are common with older adults, and the determination of clinically significant change across time is an important issue. Regression-based prediction formulas have been utilized with other patient and healthy control samples to predict follow-up test performance based on initial performance and demographic variables. Comparisons between predicted and observed follow-up performances can assist clinicians in making the determination of change in the individual patient. The current study developed regression-based prediction equations for the twelve subtests of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) in a sample of 223 community dwelling older adults. All algorithms included both initial test performances and demographic variables. These algorithms were then validated on a separate elderly sample (n = 222). Minimal differences were present between Observed and Predicted follow-up scores in the Validation sample, suggesting that the prediction formulas would be useful for practitioners who assess older adults. A case example is presented that utilizes the formulas.