A major use of neuropsychological assessment is
to measure changes in functioning over time; that is, to
determine whether a difference in test performance indicates
a real change in the individual or just chance
variation. Using 7 illustrative test measures and retest
data from 384 neurologically stable adults, this paper
compares different methods of predicting retest scores,
and of determining whether observed changes in performance
are unusual. The methods include the Reliable Change Index,
with and without correction for practice effect, and models
based upon simple and multiple regression. For all test
variables, the most powerful predictor of follow-up performance
was initial performance. Adding demographic variables and
overall neuropsychological competence at baseline significantly
but slightly improved prediction of all follow-up scores.
The simple Reliable Change Index without correction for
practice performed least well, with high error rates and
large prediction intervals (confidence intervals). Overall
prediction accuracy was similar for the other three methods;
however, different models produce large differences in
predicted scores for some individuals, especially those
with extremes of initial test performance, overall competency,
or demographics. All 5 measures from the Halstead–Reitan
Battery had residual (observed − predicted score)
variability that increased with poorer initial performance.
Two variables showed significant nonnormality in the distribution
of residuals. For accurate prediction with smallest
prediction–confidence intervals, we recommend multiple
regression models with attention to differential variability and
nonnormality of residuals. (JINS, 1999, 5,
357–369.)
Novel Approach to Estimate Osteoarthritis Progression: Use of the Reliable Change Index in the Evaluation of Joint Space Loss
