Related Experiment Video
Updated: Jul 4, 2025

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
Published on: March 7, 2019
Anchor-based minimal important difference values are often sensitive to the distribution of the change score
Werner Vach1,2, Franziska Saxer3,4
1Department of Environmental Sciences, University of Basel, Spalenring 145, CH-4055, Basel, Switzerland. werner.vach@unibas.ch.
Purpose:
Anchor-based studies are today the most popular approach to determine a minimal important difference value for an outcome variable. However, a variety of construction methods for such values do exist. This constitutes a challenge to the field. In order to distinguish between more or less adequate construction methods, meaningful minimal requirements can be helpful. For example, minimal important difference values should not reflect the intervention(s) the patients are exposed to in the study used for construction, as they should later allow to compare interventions. This requires that they are not sensitive to the distribution of the change score observed. This study aims at investigating to which degree established construction methods fulfil this minimal requirement.
Methods:
Six constructions methods were considered, covering very popular and recently suggested methods. The sensitivity of MID values to the distribution of the change score was investigated in a simulation study for these six construction methods.
Results:
Five out of six construction methods turned out to yield MID values which are sensitive to the distribution of the change score to a degree that questions their usefulness. Insensitivity can be obtained by using construction methods based solely on an estimate of the conditional distribution of the anchor variable given the change score.
Conclusion:
In future the computation of MID values should be based on construction methods avoiding sensitivity to the distribution of the change score.
Related Concept Videos
Significance Testing: Overview
Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Regression Toward the Mean
Critical Values
Sign Test for Matched Pairs
To conduct the sign test, we first calculate the differences in...
Bonferroni Test
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...

