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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.
Insights
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.
Introduction And Motivation:
Many health studies measure a continuous outcome and compare means between groups. Since means for biological data are often difficult to interpret clinically, it is common to dichotomise using a cut-point and present the 'percentage abnormal' alongside or in place of means. Examples include birthweight where 'abnormal' is defined as <2500 g (low birthweight), systolic blood pressure with abnormal defined as >140 mm Hg (high blood pressure) and lung function with varying definitions of the 'limit of normal'. In vulnerable populations with low means, for example, birthweight in a population of preterm babies, a given difference in means between two groups will represent a larger difference in the percentage with low birthweight than in a general population of babies where most will be full term. Thus, in general, the difference in percentage of patients with abnormal values for a given difference in means varies according to the reference population's mean value. This phenomenon leads to challenges in interpreting differences in means in vulnerable populations and in defining an outcome-specific minimal clinically important difference (MCID) in means since the proportion abnormal, which is useful in interpreting means, is not constant-it varies with the population mean. This has relevance for study power calculations and data analyses in vulnerable populations where a small observed difference in means may be difficult to interpret clinically and may be disregarded, even if associated with a relatively large difference in percentage abnormal which is clinically relevant.
Methods:
To address these issues, we suggest both difference in means and difference in percentage (proportion) abnormal are considered when choosing the MCID, and that both means and percentages abnormal are reported when analysing the data.
Conclusions:
We describe a distributional approach to analyse proportions classified as abnormal that avoids the usual loss of precision and power associated with dichotomisation.
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