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Objective: Reliable change methods can aid in determining whether changes in cognitive performance over time are meaningful. The current study sought to develop and cross-validate 12-month standardized regression-based (SRB) equations for the neuropsychological measures commonly administered in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) longitudinal study. Method: Prediction algorithms were developed using baseline score, retest interval, the presence/absence of a 6-month evaluation, age, education, sex, and ethnicity in two different samples ( n  = 192 each) of robustly cognitively intact community-dwelling older adults from ADNI – matched for demographic and testing factors. The developed formulae for each sample were then applied to one of the samples to determine goodness-of-fit and appropriateness of combining samples for a single set of SRB equations. Results: Minimal differences were seen between Observed 12-month and Predicted 12-month scores on most neuropsychological tests from ADNI, and when compared across samples the resultant Predicted 12-month scores were highly correlated. As a result, samples were combined and SRB prediction equations were successfully developed for each of the measures. Conclusions: Establishing cross-validation for these SRB prediction equations provides initial support of their use to detect meaningful change in the ADNI sample, and provides the basis for future research with clinical samples to evaluate potential clinical utility. While some caution should be considered for measuring true cognitive change over time – particularly in clinical samples – when using these prediction equations given the relatively lower coefficients of stability observed, use of these SRBs reflects an improvement over current practice in ADNI.

Assessing and validating reliable change across ADNI protocols