ArticleStatistics in medicine2024
Unlocking Cognitive Analysis Potential in Alzheimer's Disease Clinical Trials: Investigating Hierarchical Linear Models for Analyzing Novel Measurement Burst Design Data.
Article in Statistics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Developing a psychological test battery to measure cognition in daily life.Behavior research methods · 2026Article
- Statistical models for Alzheimer's disease clinical trials: Lessons learned from the DIAN-TU Platform Trial.Journal of Alzheimer's disease : JAD · 2026Article
- Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.PloS one · 2025Article
- Remote digital cognitive assessment in a trial-ready Alzheimer's disease cohort: A scalable approach for early intervention studies.Alzheimer's & dementia (New York, N. Y.)Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Measurement burst designs typically administer brief cognitive tests four times per day for 1 week, resulting in a maximum of 28 data points per week per test for every 6 months. In Alzheimer's disease clinical trials, utilizing measurement burst designs holds great promise for boosting statistical power by collecting huge amount of data. However, appropriate methods for analyzing these complex datasets are not well investigated. Furthermore, the large amount of burst design data also poses tremendous challenges for traditional computational procedures such as SAS mixed or Nlmixed. We propose to analyze burst design data using novel hierarchical linear mixed effects models or hierarchical mixed models for repeated measures. Through simulations and real-world data applications using the novel SAS procedure Hpmixed, we demonstrate these hierarchical models' efficiency over traditional models. Our sample simulation and analysis code can serve as a catalyst to facilitate the methodology development for burst design data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.