Predicting cognitive change across 3 years in community-dwelling elders

The determination of clinically significant cognitive change across time is an important issue in neuropsychology, and repeated assessments are common with older adults. Regression-based prediction formulas, which use initial test performance and demographic variables to predict follow-up test performance, have been utilized with patient and healthy control samples. Comparisons between predicted and observed follow-up performances can assist clinicians in determining the significance of change in the individual patient. In the current study, multiple regression-based prediction equations for the five Indexes and Total Score of the RBANS were developed for a sample of 146 community-dwelling older adults across a 2-year interval. These algorithms were then validated on a separate elderly sample (n = 145). Minimal differences were present between Observed and Predicted follow-up scores in the validation sample, suggesting that the prediction formulas are clinically useful for practitioners who assess older adults. A case example is presented that illustrates how the algorithms can be used clinically.