An examination of reliable change methods for measuring cognitive change with the Cogstate Computerized Battery: Research and clinical implications

Author:

Gates Thomas M12ORCID,Kamminga Jody3,Jayewardene Avindra4,Vincent Trina4,Quan Dick4,Brew Bruce J125,Bloch Mark46,Cysique Lucette A1237

Affiliation:

1. Departments of Neurology and HIV Medicine, St Vincent's Hospital, Sydney, Australia

2. Peter Duncan Neurosciences Unit, St Vincent's Centre for Applied Medical Research, Sydney, Australia

3. Neuroscience Research Australia, Sydney, Australia

4. Holdsworth House Medical Practice, Sydney, Australia

5. St Vincent’s Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia

6. Faculty of Medicine, University of New South Wales, Sydney, Australia

7. School of Psychology, University of New South Wales, Sydney, Australia

Abstract

Abstract Objective To compare the performance of four reliable change (RC) methods with respect to measuring cognitive change on the Cogstate Computerized Battery (CCB). Method We assessed cognitive change in 57 healthy, urban, well-educated males on the CCB at baseline and 6 months (Median age = 50, 65% university-educated). The study CCB version comprised seven measures covering attention, processing speed, verbal learning, and memory. Raw scores were z-score transformed using age-corrected Cogstate norms (CN) or the sample mean and standard deviation (internal standardization [IS]), and then averaged to create composite z-scores. Composite scores were entered into four RC formulae. RC was defined based on a 90% two-tailed confidence interval. Change scores were compared as continuous (z-scores) and ordinal variables (RC outcomes). Results CCB composite score reliability (rXY = .78–.79) was replicated in an age- and sex-matched Cogstate database sample of similar size. There was good overall agreement between the four RC methods (Bland–Altman Mdiff = .00; 95% limits of agreement with the mean—CN: z = ± .90; IS: z = ± .93), with each model adhering closely to the 10% rate of RC expected by chance alone (largest χ2 = .86, p = .99). Initial norming strategy (CN or IS) did not affect these outcomes. Conclusions Norming strategy and RC method choice did not significantly impact cognitive change predictions on CCB composite scores. A series of example case data are provided to practically demonstrate the steps involved in applying the longitudinal norms generated in this study. Research in more diverse normative samples is warranted.

Funder

ViiV Healthcare

Publisher

Oxford University Press (OUP)

Subject

Psychiatry and Mental health,Clinical Psychology,Neuropsychology and Physiological Psychology,General Medicine

Reference68 articles.

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