ArticleAlzheimer's & dementia (New York, N. Y.)
The problem of multiple adjustments in the assessment of minimal clinically important differences.
Article in Alzheimer's & dementia (New York, N. Y.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Integrating genetic and immune profiles for personalized immunotherapy in Alzheimer's disease.Frontiers in medicine · 2025Review
- The problem of multiple adjustments in the assessment of minimal clinically important differences.Alzheimer's & dementia (New York, N. Y.)Article
- Exploring precision medicine by utilizing individual genetic information for the management of Alzheimer's disease.PharmacogenomicsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAnthropometric, demographic, genetic, and clinical features may affect cognitive, behavioral, and functional decline, while clinical trials seldom consider minimal clinically important differences (MCIDs) in their analyses.
methodsMCIDs were reviewed taking into account features that may affect cognitive, behavioral, or functional decline in clinical trials of new disease-modifying therapies.
resultsThe higher the number of comparisons of different confounders in statistical analyses, the lower DISCUSSION: Statistical adjustment of the significance of MCIDs according to multiple comparisons is essential for the generalizability of research results. Wider inclusion of confounding variables in the statistics may help bring trial results closer to real-world conditions and improve the prediction of the efficacy of new disease-modifying therapies, though such factors must be carefully selected not to compromise the statistical significance of the analyses. Highlights: Anthropometric, demographic, and clinical features may affect cognitive, behavioral, and functional decline.Clinical trials seldom take minimal clinically important differences (MCIDs) or their confounders into account.Generalizability of research results requires the assessment of multiple confounding factors.The higher the number of comparisons involved, the lower
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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.