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The problem of multiple adjustments in the assessment of minimal clinically important differences
Fabricio Ferreira de Oliveira1
1Escola Paulista de Medicina Federal University of São Paulo (UNIFESP) São Paulo Brazil.
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.
Insights
Clinical trials must account for confounding factors to accurately assess minimal clinically important differences (MCIDs). Adjusting MCID significance for multiple comparisons enhances research generalizability and real-world treatment predictions.
Area of Science:
- Clinical research methodology
- Statistical analysis in medicine
- Disease-modifying therapies
Background:
- Cognitive, behavioral, and functional decline are influenced by various patient characteristics.
- Clinical trials often overlook minimal clinically important differences (MCIDs) and their confounding factors.
Purpose of the Study:
- To review minimal clinically important differences (MCIDs) considering factors affecting decline in disease-modifying therapy trials.
- To emphasize the importance of confounder selection for accurate MCID assessment.
Main Methods:
- Review of MCID assessment in clinical trials for new disease-modifying therapies.
- Analysis of the impact of confounding variables on statistical significance.
Main Results:
- Increased comparisons of confounders lower P-values, necessitating careful selection.
- Proper confounder selection is vital for accurate MCID evaluation without compromising statistical significance.
Conclusions:
- Adjusting MCID significance for multiple comparisons is crucial for research generalizability.
- Incorporating carefully selected confounding variables can improve real-world relevance and efficacy prediction of therapies.
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