Minimal clinically important difference in means in vulnerable populations: challenges and solutions

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.