Harmonizing the Preclinical Alzheimer Cognitive Composite for Multicohort Studies

Objectives: Studies are increasingly examining research questions across multiple cohorts using data from the preclinical Alzheimer cognitive composite (PACC). Our objective was to use modern psychometric approaches to develop a harmonized PACC. Method: We used longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Harvard Aging Brain Study (HABS), and Australian Imaging, Biomarker and Lifestyle Study of Ageing (AIBL) cohorts (n = 2,712). We further demonstrated our method with the Anti-Amyloid Treatment of Asymptomatic Alzheimer's Disease (A4) Study prerandomized data (n = 4,492). For the harmonization method, we used confirmatory factor analysis (CFA) on the final visit of the longitudinal cohorts to determine parameters to generate latent PACC (lPACC) scores. Overlapping tests across studies were set as "anchors" that tied cohorts together, while parameters from unique tests were freely estimated. We performed validation analyses to assess the performance of lPACC versus the common standardized PACC (zPACC). Results: Baseline (BL) scores for the zPACC were centered on zero, by definition. The harmonized lPACC did not define a common mean of zero and demonstrated differences in baseline ability levels across the cohorts. Baseline lPACC slightly outperformed zPACC in the prediction of progression to dementia. Longitudinal change in the lPACC was more constrained and less variable relative to the zPACC. In combined-cohort analyses, longitudinal lPACC slightly outperformed longitudinal zPACC in its association with baseline beta-amyloid status. Conclusions: This study proposes procedures for harmonizing the PACC that make fewer strong assumptions than the zPACC, facilitating robust multicohort analyses. This implementation of item response theory lends itself to adapting across future cohorts with similar composites.