ArticleAlzheimer's & dementia (New York, N. Y.)2019
Two-period linear mixed effects models to analyze clinical trials with run-in data when the primary outcome is continuous: Applications to Alzheimer's disease.
Article in Alzheimer's & dementia (New York, N. Y.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Real-World Before-After Study ofNutrients · 2026Article
- Sample size estimates for biomarker-based outcome measures in clinical trials in autosomal dominant Alzheimer's disease.The journal of prevention of Alzheimer's disease · 2025Article
- Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.PloS one · 2025Article
- Pune GSH supplementation study: Analyzing longitudinal changes in type 2 diabetic patients using linear mixed-effects models.Frontiers in pharmacology · 2023Article
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10 authors.
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Abstract
introductionStudy outcomes can be measured repeatedly based on the clinical trial protocol before randomization during what is known as the "run-in" period. However, it has not been established how best to incorporate run-in data into the primary analysis of the trial.
methodsWe proposed two-period (run-in period and randomization period) linear mixed effects models to simultaneously model the run-in data and the postrandomization data.
resultsCompared with the traditional models, the two-period linear mixed effects models can increase the power up to 15% and yield similar power for both unequal randomization and equal randomization. DISCUSSION: Given that analysis of run-in data using the two-period linear mixed effects models allows more participants (unequal randomization) to be on the active treatment with similar power to that of the equal-randomization trials, it may reduce the dropout by assigning more participants to the active treatment and thus improve the efficiency of AD clinical trials.
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