Optimal Weighting of Preclinical Alzheimer’s Cognitive Composite (PACC) Scales to Improve their Performance as Outcome Measures for Alzheimer’s Disease Clinical Trials

Xinran Wang; Diane  Jacobs; David P.  Salmon; Howard H.  Feldman; Steven D.  Edland

doi:10.6000/1929-6029.2023.12.12

Authors

Xinran Wang Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA
Diane Jacobs Department of Neurosciences, School of Medicine, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA and Alzheimer’s Disease Cooperative Study, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA
David P. Salmon Department of Neurosciences, School of Medicine, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA and Alzheimer’s Disease Cooperative Study, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA
Howard H. Feldman Department of Neurosciences, School of Medicine, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA and Alzheimer’s Disease Cooperative Study, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA https://orcid.org/0000-0002-9258-4538
Steven D. Edland Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA; Department of Neurosciences, School of Medicine, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA and Alzheimer’s Disease Cooperative Study, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA https://orcid.org/0000-0002-1153-7335

DOI:

https://doi.org/10.6000/1929-6029.2023.12.12

Keywords:

Cognitive composite, statistical power, clinical trial design, clinical trial endpoints

Abstract

Introduction: Cognitive composite scales constructed by combining existing neuropsychometric tests are seeing wide application as endpoints for clinical trials and cohort studies of Alzheimer’s disease (AD) predementia conditions. Preclinical Alzheimer’s Cognitive Composite (PACC) scales are composite scores calculated as the sum of the component test scores weighted by the reciprocal of their standard deviations at the baseline visit. Reciprocal standard deviation is an arbitrary weighting in this context, and may be an inefficient utilization of the data contained in the component measures. Mathematically derived optimal composite weighting is a promising alternative.

Methods: Sample size projections using standard power calculation formulas were used to describe the relative performance of component measures and their composites when used as endpoints for clinical trials. Power calculations were informed by (n=1,333) amnestic mild cognitive impaired participants in the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set.

Results: A composite constructed using PACC reciprocal standard deviation weighting was both less sensitive to change than one of its component measures and less sensitive to change than its optimally weighted counterpart. In standard sample size calculations informed by NACC data, a clinical trial using the PACC weighting would require 38% more subjects than a composite calculated using optimal weighting.

Discussion: These findings illustrate how reciprocal standard deviation weighting can result in inefficient cognitive composites, and underscore the importance of component weights to the performance of composite scales. In the future, optimal weighting parameters informed by accumulating clinical trial data may improve the efficiency of clinical trials in AD.

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