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...

Full description

Saved in:
Bibliographic Details
Published in:Archives of clinical neuropsychology 2005-05, Vol.20 (3), p.281-290
Main Authors: Duff, Kevin, Schoenberg, Mike R., Patton, Doyle, Paulsen, Jane S., Bayless, John D., Mold, James, Scott, James G., Adams, Russell L.
Format: Article
Language:English
Subjects:
Age Factors
Aged
Algorithms
Biological and medical sciences
Cognition Disorders - diagnosis
Developmental psychology
Female
Follow-Up Studies
Fundamental and applied biological sciences. Psychology
Geriatric Assessment
Humans
Male
Neuropsychological Tests
Older adults
Prediction
Predictive Value of Tests
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometric change
Psychometrics
RBANS
Regression Analysis
Reproducibility of Results
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0887-6177
1873-5843
DOI:10.1016/j.acn.2004.07.007