Importance of minimal clinically important difference in medical research and guideline development
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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.Abstract
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Manya Prasad, Institute of Liver and Biliary Sciences, New Delhi
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Wasserstein RL, Lazar NA. The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician. 2016;70(2):129–33. 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. 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. 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. 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. Mauri L, D'Agostino RB Sr. Challenges in the Design and Interpretation of Noninferiority Trials. N Engl J Med. 2017;377(14):1357-1367. 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. 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.
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