Improving the interpretation of quality of life evidence in meta-analyses: the application of minimal important difference units

Abstract

Systematic reviews of randomized trials that include measurements of health-related quality of life potentially provide critical information for patient and clinicians facing challenging health care decisions. When, as is most often the case, individual randomized trials use different measurement instruments for the same construct (such as physical or emotional function), authors typically report differences between intervention and control in standard deviation units (so-called "standardized mean difference" or "effect size"). This approach has statistical limitations (it is influenced by the heterogeneity of the population) and is non-intuitive for decision makers. We suggest an alternative approach: reporting results in minimal important difference units (the smallest difference patients experience as important). This approach provides a potential solution to both the statistical and interpretational problems of existing methods.

Facet joint injections for people with persistent non-specific low back pain (Facet Injection Study): a feasibility study for a randomised controlled trial

“…88,94,95 An alternative approach that takes into consideration meaningful individual-level change is afforded by the calculation of the MID unit. 96,97 The MID unit divides the between-group difference found in a trial by the established MIC for the outcome of interest: estimates of < 0.5 MID units suggest that it is increasingly less likely that an appreciable number of patients will achieve important benefits from treatment, whereas values between 0.5 and 1.0 suggest that treatment may benefit an appreciable number of patients. This approach, increasingly used within meta-analyses of trial evidence, 96,97 grounds the calculation in clinical reality (a within-person individual change) while tailoring the MID to the nature of the intervention.…”

“…96,97 The MID unit divides the between-group difference found in a trial by the established MIC for the outcome of interest: estimates of < 0.5 MID units suggest that it is increasingly less likely that an appreciable number of patients will achieve important benefits from treatment, whereas values between 0.5 and 1.0 suggest that treatment may benefit an appreciable number of patients. This approach, increasingly used within meta-analyses of trial evidence, 96,97 grounds the calculation in clinical reality (a within-person individual change) while tailoring the MID to the nature of the intervention. Interpretation provides an evaluation of whether or not an appreciable number of patients achieve clinically important benefits, with MID units of < 1 reflecting increasingly lower likelihood of benefit.…”

Do clinicians understand the size of treatment effects? A randomized survey across 8 countries

“…4 For meta-analyses of outcomes measured using different instruments, presenting results as an SMD is the longest standing and most widely used approach and is recommended in the Cochrane handbook for systematic reviews of interventions. 4 Limitations of this approach include, however, statistical bias toward decreased treatment effects, 5,6 the possibility that decision-makers will find the measure difficult to interpret 7,8 and the possibility that the same treatment effect will appear different depending on whether the study population had similar results in the measure of interest (i.e., if homogeneous, a small standard deviation) or varied greatly in the measure of interest (i.e., if heterogeneous, a large standard deviation). 9,10 Several research groups have proposed alternative statistical formats for presenting continuous outcomes from meta-analyses that they postulate clinicians will more easily interpret.…”

“…9,10 Several research groups have proposed alternative statistical formats for presenting continuous outcomes from meta-analyses that they postulate clinicians will more easily interpret. [6][7][8][11][12][13][14][15][16] The Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group recently provided an overview of methods for presenting pooled continuous data. 9,10 These alternatives (Appendix 1, available at www.cmaj.ca/lookup/suppl/doi: 10.1503/ cmaj.150430/-/DC1), although intuitively compelling, have seen limited use.…”

New methods can extend the use of minimal important difference units in meta-analyses of continuous outcome measures

“…More recently, we have described an approach of reporting in MID units the pooled effects from meta-analysis of RCTs using more than one HRQL measure [3].…”