Importance of minimal clinically important difference in medical research and guideline development
DOI:
https://doi.org/10.56450/JEFI.2024.v2i01.001Abstract
P-values have posed various challenges in conducting and interpreting medical research. In an endeavor to establish more objective criteria for assessing outcomes in medical care, statistical methods have been utilized to analyze clinical trial results, often leading to a perceived dichotomy: trial outcomes are categorized as either positive or negative based on a p-value. Unfortunately, clinicians began to overly rely on the statistical significance of studies, misinterpreting their findings as clinically meaningful. Recognizing the detrimental effects of p-values, the American Statistical Association advised against their use in scientific publications (1). Instead, emphasis should be placed on the magnitude of difference between intervention and control groups. Prior to conducting a study, and in assessing the results of a body of evidence it is essential to estimate the minimum size of the difference that would be clinically significant. The smallest magnitude of benefit that patients would deem as clinically important is the minimal clinically important difference (MCID) (2). The MCID encapsulates a patient-centered approach, encompassing both the degree of improvement and the value patients attribute to this change.
Downloads
References
1. Wasserstein RL, Lazar NA. The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician. 2016;70(2):129–33. DOI: https://doi.org/10.1080/00031305.2016.1154108
2. McGlothlin AE, Lewis RJ. Minimal clinically important difference: defining what really matters to patients. JAMA. 2014;312(13):1342-3. doi: 10.1001/jama.2014.13128. PMID: 25268441. DOI: https://doi.org/10.1001/jama.2014.13128
3. Carrasco-Labra A, Devji T, Qasim A, Phillips MR, Wang Y, Johnston BC, et al. Minimal important difference estimates for patient-reported outcomes: A systematic survey. J Clin Epidemiol. 2021;133:61-71. DOI: https://doi.org/10.1016/j.jclinepi.2020.11.024
4. Schünemann HJ, Neumann I, Hultcrantz M, Brignardello-Petersen R, Zeng L, Murad MH, et al; GRADE Working Group. GRADE guidance 35: update on rating imprecision for assessing contextualized certainty of evidence and making decisions. J Clin Epidemiol. 2022;150:225-242. DOI: https://doi.org/10.1016/j.jclinepi.2022.07.015
5. Alonso-Coello P, Schünemann HJ, Moberg J, Brignardello-Petersen R, Akl EA, Davoli M, et al; GRADE Working Group. GRADE Evidence to Decision (EtD) frameworks: a systematic and transparent approach to making well informed healthcare choices. 1: Introduction. BMJ. 2016 Jun 28;353:i2016. DOI: https://doi.org/10.1136/bmj.i2016
6. Mauri L, D'Agostino RB Sr. Challenges in the Design and Interpretation of Noninferiority Trials. N Engl J Med. 2017;377(14):1357-1367. DOI: https://doi.org/10.1056/NEJMra1510063
7. Wang Y, Devji T, Qasim A, Hao Q, Wong V, Bhatt M, Prasad M, Wang Y, Noori A, Xiao Y, Ghadimi M, Lozano LEC, Phillips MR, Carrasco-Labra A, King M, Terluin B, Terwee CB, Walsh M, Furukawa TA, Guyatt GH. A systematic survey identified methodological issues in studies estimating anchor-based minimal important differences in patient-reported outcomes. J Clin Epidemiol. 2022;142:144-151. DOI: https://doi.org/10.1016/j.jclinepi.2021.10.028
8. Tsujimoto Y, Fujii T, Tsutsumi Y, Kataoka Y, Tajika A, Okada Y, Carrasco-Labra A, Devji T, Wang Y, Guyatt GH, Furukawa TA. Minimal important changes in standard deviation units are highly variable and no universally applicable value can be determined. J Clin Epidemiol. 2022;145:92-100. DOI: https://doi.org/10.1016/j.jclinepi.2022.01.017
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Manya Prasad (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
