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Minimal clinically important difference in means in vulnerable populations: challenges and solutions
Janet L Peacock1, Jessica Lo2, Judith R Rees3
1Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA janet.peacock@dartmouth.edu.
BMJ Open
|November 10, 2021
Summary
Health studies comparing means should also report the percentage of abnormal values, especially in vulnerable populations. This dual reporting aids clinical interpretation and avoids misinterpreting small mean differences.
Area of Science:
- Biostatistics
- Clinical Research
- Health Outcomes
Background:
- Health studies often compare continuous outcomes using means.
- Clinical interpretation of means can be challenging, leading to dichotomization and reporting 'percentage abnormal'.
- This dichotomization poses challenges in vulnerable populations where mean differences may not directly reflect clinically significant changes in abnormal proportions.
Purpose of the Study:
- To address the challenges in interpreting mean differences in health studies, particularly in vulnerable populations.
- To propose a method for choosing a minimal clinically important difference (MCID) that considers both mean differences and differences in the percentage of abnormal values.
- To advocate for reporting both means and percentages of abnormal values in data analysis.
Main Methods:
- Suggesting the consideration of both difference in means and difference in percentage abnormal when selecting the MCID.
- Recommending the reporting of both means and percentages abnormal during data analysis.
- Describing a distributional approach to analyze proportions classified as abnormal, preserving precision and power.
Main Results:
- A given difference in means can represent varying differences in the percentage of abnormal values depending on the population's mean.
- Small observed differences in means in vulnerable populations may be disregarded despite clinically relevant differences in percentage abnormal.
- The proposed approach aims to improve the interpretation of clinical significance in vulnerable populations.
Conclusions:
- Both difference in means and difference in percentage abnormal should be considered for MCID selection.
- Reporting both means and percentages abnormal is crucial for comprehensive data analysis and interpretation.
- A distributional approach offers a more precise and powerful method for analyzing abnormal proportions.
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