title: Predicting cognitive change within domains.
abstract: Standardized regression based (SRB) formulas, a method for predicting cognitive change across time, traditionally use baseline performance on a neuropsychological measure to predict future performance on that same measure. However, there are instances in which the same tests may not be given at follow-up assessments (e.g., lack of continuity of provider, avoiding practice effects). The current study sought to expand this methodology by developing SRBs to predict performance on different tests within the same cognitive domain. Using a sample of 127 non-demented community-dwelling older adults assessed at baseline and after 1 year, two sets of SRBs were developed: (1) those predicting performance on the same test, and (2) those predicting performance on a different test within the same cognitive domain. The domains examined were learning and memory, processing speed, and language. Across both sets of SRBs, 1-year scores were significantly predicted by baseline scores, especially for the learning and memory and processing speed measures. Although SRBs developed for the same test were comparable to those developed for different tests within the same domain, less variance was accounted for as tests became less similar. The current results lend preliminary support for additional development of SRBs, both for same and different tests, as well as beginning to examine domain-based SRBs.
authors.affiliation: Department of Neurology, University of Utah, Salt Lake City, UT 84108, USA. kevin. duff@hsc.utah.edu
authors.forename: Kevin
authors.initials: K
authors.lastname: Duff
authors.forename: Leigh J
authors.initials: LJ
authors.lastname: Beglinger
authors.forename: David J
authors.initials: DJ
authors.lastname: Moser
authors.forename: Jane S
authors.initials: JS
authors.lastname: Paulsen
mesh_terms: D000368:Aged; D003071:Cognition; D005260:Female; D006801:Humans; D008297:Male; D009483:Neuropsychological Tests / Q000592:standards*; D011237:Predictive Value of Tests; D012044:Regression Analysis; D015203:Reproducibility of Results