Abstract

PurposeThe minimal important change (MIC) of a patient-reported outcome measure (PROM) is often suspected to be baseline dependent, typically in the sense that patients who are in a poorer baseline health condition need greater improvement to qualify as minimally important. Testing MIC baseline dependency is commonly performed by creating two or more subgroups, stratified on the baseline PROM score. This study’s purpose was to show that this practice produces biased subgroup MIC estimates resulting in spurious MIC baseline dependency, and to develop alternative methods to evaluate MIC baseline dependency.MethodsDatasets with PROM baseline and follow-up scores and transition ratings were simulated with and without MIC baseline dependency. Mean change MICs, ROC-based MICs, predictive MICs, and adjusted MICs were estimated before and after stratification on the baseline score. Three alternative methods were developed and evaluated. The methods were applied in a real data example for illustration.ResultsBaseline stratification resulted in biased subgroup MIC estimates and the false impression of MIC baseline dependency, due to redistribution of measurement error. Two of the alternative methods require a second baseline measurement with the same PROM or another correlated PROM. The third method involves the construction of two parallel tests based on splitting the PROM’s item set. Two methods could be applied to the real data.ConclusionMIC baseline dependency should not be tested in subgroups based on stratification on the baseline PROM score. Instead, one or more of the suggested alternative methods should be used.

Highlights

  • The minimal important change (MIC) is defined as the smallest change in a patient-reported outcome measure (PROM) that is important to patients [1, 2]

  • The simulations show that stratifying on the baseline score, either by median-splitting or by resampling, results in spurious baseline dependency of the MICs

  • At this point it is important to recognize that the ROC and predictive modeling methods target a different MIC concept than the mean change method

Read more

Summary

Introduction

The minimal important change (MIC) is defined as the smallest change in a patient-reported outcome measure (PROM) that is important to patients [1, 2]. Anchor-based MICs correspond to an external criterion (the “anchor”) of what constitutes a minimal important change for patients. This external criterion is often a transition question, asking patients to rate their perceived change between two moments in time [3]. The change of interest can be in the direction of improvement or deterioration. We will limit the present treatise to improvement, knowing that the case for deterioration is exactly the reverse. Three commonly applied methods to estimate anchor-based MICs are the mean change method, the receiver operating characteristic

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call