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

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.

References

Donohue MC, Sperling RA, Salmon DP, Rentz DM, Raman R, Thomas RG, et al. The preclinical Alzheimer cognitive composite: measuring amyloid-related decline. JAMA Neurol 2014; 71(8): 961-70. https://doi.org/10.1001/jamaneurol.2014.803

Brain Energy for Amyloid Transformation in Alzheimer's Disease Study (BEAT-AD) clinicaltrials.gov [Available from: https://clinicaltrials.gov/ct2/show/NCT03472664.

Pomilio C, Perez NG, Calandri I, Crivelli L, Allegri R, Initiative AAsDN, et al. Diabetic patients treated with metformin during early stages of Alzheimer's disease show a better integral performance: data from ADNI study. Geroscience 2022; 44(3): 1791-805. https://doi.org/10.1007/s11357-022-00568-6

Papp KV, Rentz DM, Orlovsky I, Sperling RA, Mormino EC. Optimizing the preclinical Alzheimer's cognitive composite with semantic processing: The PACC5. Alzheimers Dement (N Y) 2017; 3(4): 668-77. https://doi.org/10.1016/j.trci.2017.10.004

Nuno MM, Gillen DL, Grill JD, Alzheimer's Disease Cooperative S. Study partner types and prediction of cognitive performance: implications to preclinical Alzheimer's trials. Alzheimers Res Ther 2019; 11(1): 92. https://doi.org/10.1186/s13195-019-0544-6

Wang Q, Chen G, Schindler SE, Christensen J, McKay NS, Liu J, et al. Baseline Microglial Activation Correlates With Brain Amyloidosis and Longitudinal Cognitive Decline in Alzheimer Disease. Neurol Neuroimmunol Neuroinflamm 2022; 9(3). https://doi.org/10.1212/NXI.0000000000001152

Forcano L, Fauria K, Soldevila-Domenech N, Minguillon C, Lorenzo T, Cuenca-Royo A, et al. Prevention of cognitive decline in subjective cognitive decline APOE epsilon4 carriers after EGCG and a multimodal intervention (PENSA): Study design. Alzheimers Dement (N Y) 2021; 7(1): e12155. https://doi.org/10.1002/trc2.12155

Porter T, Burnham SC, Savage G, Lim YY, Maruff P, Milicic L, et al. A Polygenic Risk Score Derived From Episodic Memory Weighted Genetic Variants Is Associated With Cognitive Decline in Preclinical Alzheimer's Disease. Front Aging Neurosci 2018; 10: 423. https://doi.org/10.3389/fnagi.2018.00423

clinicaltrials.gov. A Study to Evaluate the Efficacy and Safety of Gantenerumab in Participants at Risk for or at the Earliest Stages of Alzheimer's Disease (AD) 2022 [Available from: https://www.clinicaltrials.gov/ct2/show/NCT05256134?term= NCT05256134&draw=2&rank=1.

Edland SD, Ard MC, Li W, Jiang L. Design of pilot studies to inform the construction of composite outcome measures. Alzheimers Dement (N Y) 2017; 3(2): 213-8. https://doi.org/10.1016/j.trci.2016.12.004

Longitudinal data: The Uniform Data Set [Available from: https://naccdata.org/requesting-data/nacc-data.

Mallinckrodt CH, Lane PW, Schnell D, Peng Y, Mancuso JP. Recommendations for the Primary Analysis of Continuous Endpoints in Longitudinal Clinical Trials”. Ther Innov Regul Sci 2008; 42: 303-19. https://doi.org/10.1177/009286150804200402

Edland SD, Ard MC, Sridhar J, Cobia D, Martersteck A, Mesulam MM, et al. Proof of concept demonstration of optimal composite MRI endpoints for clinical trials. Alzheimers Dement (N Y) 2016; 2(3): 177-81. https://doi.org/10.1016/j.trci.2016.05.002

Xiong C, van Belle G, Chen K, Tian L, Luo J, Gao F, et al. Combining Multiple Markers to Improve the Longitudinal Rate of Progression-Application to Clinical Trials on the Early Stage of Alzheimer's Disease. Stat Biopharm Res 2013; 5(1). https://doi.org/10.1080/19466315.2012.756662

Ard MC, Raghavan N, Edland SD. Optimal composite scores for longitudinal clinical trials under the linear mixed effects model. Pharm Stat 2015; 14(5): 418-26. https://doi.org/10.1002/pst.1701

Kukull WA, Ganguli M. Clinic-based data serving Population Neuroscience: NACC example. Alzheimer's & Dementia 2021; 17(S6): e051214. https://doi.org/10.1002/alz.051214

Jacobs DM, Ard MC, Salmon DP, Galasko DR, Bondi MW, Edland SD. Potential implications of practice effects in Alzheimer's disease prevention trials. Alzheimers Dement (N Y) 2017; 3(4): 531-5. https://doi.org/10.1016/j.trci.2017.08.010

van Dyck CH, Swanson CJ, Aisen P, Bateman RJ, Chen C, Gee M, et al. Lecanemab in Early Alzheimer's Disease. N Engl J Med 2023; 388(1): 9-21. https://doi.org/10.1056/NEJMoa2212948

Sims JR, Zimmer JA, Evans CD, Lu M, Ardayfio P, Sparks J, et al. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial. JAMA 2023; 330(6): 512-27. https://doi.org/10.1001/jama.2023.13239

Edland SD, Raghavan N, Ard MC. Counterpoint to Jin et al. On weighted composite scores for early Alzheimer's trials. Pharm Stat 2019; 18(2): 239-47. Pharm Stat 2020; 19(4): 492-3. https://doi.org/10.1002/pst.2007

Downloads

Published

2023-09-07

How to Cite

Wang, X., Jacobs, D. ., Salmon, D. P. ., Feldman, H. H. ., & Edland, S. D. . (2023). Optimal Weighting of Preclinical Alzheimer’s Cognitive Composite (PACC) Scales to Improve their Performance as Outcome Measures for Alzheimer’s Disease Clinical Trials. International Journal of Statistics in Medical Research, 12, 90–96. https://doi.org/10.6000/1929-6029.2023.12.12

Issue

Vol. 12 (2023)

Section

General Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Policy for Journals/Articles with Open Access

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  • Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work

Policy for Journals / Manuscript with Paid Access

Authors who publish with this journal agree to the following terms:

  • Publisher retain copyright .

  • Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
Crossref
Scopus
Google Scholar
Europe PMC