ArticleInternational journal of statistics in medical research2023
Optimal Weighting of Preclinical Alzheimer's Cognitive Composite (PACC) Scales to Improve their Performance as Outcome Measures for Alzheimer's Disease Clinical Trials.
Article in International journal of statistics in medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Predictors of extreme future time perspective change in persons who learn an Alzheimer's disease biomarker test result.Aging & mental health · 2026Article
- Efficacy of 3D-printed bone graft container in the treatment of tibial plateau fractures and postoperative rehabilitation prediction model.BMC surgery · 2026Article
- Implications of practice effects for the design of Alzheimer clinical trials.Alzheimer's & dementia (New York, N. Y.)Article
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5 authors.
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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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