{"title":"The problem of multiple adjustments in the assessment of minimal clinically important differences.","authors":"Fabricio Ferreira de Oliveira","doi":"10.1002/trc2.70032","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Anthropometric, 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.</p><p><strong>Methods: </strong>MCIDs were reviewed taking into account features that may affect cognitive, behavioral, or functional decline in clinical trials of new disease-modifying therapies.</p><p><strong>Results: </strong>The higher the number of comparisons of different confounders in statistical analyses, the lower <i>P</i> values will be significant. Proper selection of confounders is crucial to accurately assess MCIDs without compromising statistical significance.</p><p><strong>Discussion: </strong>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.</p><p><strong>Highlights: </strong>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 <i>P</i> values will be considered significant.Use of MCIDs adjusted for confounding factors should be implemented when outcomes are not susceptible to translation into absolute benefits.</p>","PeriodicalId":53225,"journal":{"name":"Alzheimer''s and Dementia: Translational Research and Clinical Interventions","volume":"11 1","pages":"e70032"},"PeriodicalIF":4.9000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11696022/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alzheimer''s and Dementia: Translational Research and Clinical Interventions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/trc2.70032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Anthropometric, 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.
Methods: MCIDs were reviewed taking into account features that may affect cognitive, behavioral, or functional decline in clinical trials of new disease-modifying therapies.
Results: The higher the number of comparisons of different confounders in statistical analyses, the lower P values will be significant. Proper selection of confounders is crucial to accurately assess MCIDs without compromising statistical significance.
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 P values will be considered significant.Use of MCIDs adjusted for confounding factors should be implemented when outcomes are not susceptible to translation into absolute benefits.
期刊介绍:
Alzheimer''s & Dementia: Translational Research & Clinical Interventions (TRCI) is a peer-reviewed, open access,journal from the Alzheimer''s Association®. The journal seeks to bridge the full scope of explorations between basic research on drug discovery and clinical studies, validating putative therapies for aging-related chronic brain conditions that affect cognition, motor functions, and other behavioral or clinical symptoms associated with all forms dementia and Alzheimer''s disease. The journal will publish findings from diverse domains of research and disciplines to accelerate the conversion of abstract facts into practical knowledge: specifically, to translate what is learned at the bench into bedside applications. The journal seeks to publish articles that go beyond a singular emphasis on either basic drug discovery research or clinical research. Rather, an important theme of articles will be the linkages between and among the various discrete steps in the complex continuum of therapy development. For rapid communication among a multidisciplinary research audience involving the range of therapeutic interventions, TRCI will consider only original contributions that include feature length research articles, systematic reviews, meta-analyses, brief reports, narrative reviews, commentaries, letters, perspectives, and research news that would advance wide range of interventions to ameliorate symptoms or alter the progression of chronic neurocognitive disorders such as dementia and Alzheimer''s disease. The journal will publish on topics related to medicine, geriatrics, neuroscience, neurophysiology, neurology, psychiatry, clinical psychology, bioinformatics, pharmaco-genetics, regulatory issues, health economics, pharmacoeconomics, and public health policy as these apply to preclinical and clinical research on therapeutics.