The Challenge of Designing Stroke Trials That Change Practice: MCID vs. Sample Size and Pragmatism
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Randomized controlled trials (RCT) are the basis for evidence-based acute stroke care. For an RCT to change practice, its results have to be statistically significant and clinically meaningful. While methods to assess statistical significance are standardized and widely agreed upon, there is no clear consensus on how to assess clinical significance. Researchers often refer to the minimal clinically important difference (MCID) when describing the smallest change in outcomes that is considered meaningful to patients and leads to a change in patient management. It is widely accepted that a treatment should only be adopted when its effect on outcome is equal to or larger than the MCID. There are however situations in which it is reasonable to decide against adopting a treatment, even when its beneficial effect matches or exceeds the MCID, for example when it is resource-intensive and associated with high costs. Furthermore, while the MCID represents an important concept in this regard, defining it for an individual trial is difficult as it is highly context specific. In the following, we use hypothetical stroke trial examples to review the challenges related to MCID, sample size and pragmatic considerations that researchers face in acute stroke trials, and propose a framework for designing meaningful stroke trials that have the potential to change clinical practice.
Abstract
Randomized controlled trials (RCT) are the basis for evidence-based acute stroke care. For an RCT to change practice, its results have to be statistically significant and clinically meaningful. While methods to assess statistical significance are standardized and widely agreed upon, there is no clear consensus on how to assess clinical significance. Researchers often refer to the minimal clinically important difference (MCID) when describing the smallest change in outcomes that is considered meaningful to patients and leads to a change in patient management. It is widely accepted that a treatment should only be adopted when its effect on outcome is equal to or larger than the MCID. There are however situations in which it is reasonable to decide against adopting a treatment, even when its beneficial effect matches or exceeds the MCID, for example when it is resource-intensive and associated with high costs. Furthermore, while the MCID represents an important concept in this regard, defining it for an individual trial is difficult as it is highly context specific. In the following, we use hypothetical stroke trial examples to review the challenges related to MCID, sample size and pragmatic considerations that researchers face in acute stroke trials, and propose a framework for designing meaningful stroke trials that have the potential to change clinical practice.
참고문헌 (19)
- Berkhemer OA, "A randomized trial of intraarterial treatment for acute ischemic stroke", N Engl J Med, 372, pp.11~20 (2015)
- Campbell BC, "Endovascular therapy for ischemic stroke with perfusion-imaging selection", N Engl J Med, 372, pp.1009~1018 (2015)
- Goyal M, "Randomized assessment of rapid endovascular treatment of ischemic stroke", N Engl J Med, 372, pp.1019~1030 (2015)
- Goyal M, "Endovascular thrombectomy after large-vessel ischaemic stroke : a meta-analysis of individual patient data from five randomised trials", Lancet, 387, pp.1723~1731 (2016)
- National Institute of Neurological Disorders and Strokert-PA Stroke Study Group, "Tissue plasminogen activator for acute ischemic stroke", N Engl J Med, 333, pp.1581~1587 (1995)
- Saver JL, "Stent-retriever thrombectomy after intravenous t-PA vs. t-PA alone in stroke", N Engl J Med, 372, pp.2285~2295 (2015)
- Cook JA, "DELTA2 guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial", BMJ, 363, pp.k3750~ (2018)
- de Vet HCW, "Encyclopedia of Quality of Life and Well-Being Research", pp.1007~ (2014), Springer Netherlands
- Draak T, "The minimum clinically important difference : which direction to take", Eur J Neurol, 26, pp.850~855 (2019)
- Alemayehu C, "Barriers for conducting clinical trials in developing countries : a systematic review", Int J Equity Health, 17, pp.37~ (2018)
- Chow SC, "Sample Size Calculations in Clinical Research" (2020), CRC Press
- Moher D, "The CONSORT Statement: revised recommendations for improving the quality of reports of parallel-group randomized trials 2001", Explore(NY), 1, pp.40~45 (2005)
- Walder B, "Minimal clinically important difference : a novel approach to measure changes in outcome in perioperative medicine", Eur J Anaesthesiol, 32, pp.77~78 (2015)
- Zarin DA, "Harms from uninformative clinical trials", JAMA, 322, pp.813~814 (2019)
- D’Agostino RB Sr, "Non-inferiority trials: design concepts and issues. The encounters of academic consultants in statistics", Stat Med, 22, pp.169~186 (2003)
- Fisher M, "Enhancing the development and approval of acute stroke therapies : Stroke Therapy Academic Industry roundtable", Stroke, 36, pp.1808~1813 (2005)
- Hong KS, "Weighting components of composite end points in clinical trials : an approach using disability-adjusted life-years", Stroke, 42, pp.1722~1729 (2011)
- Chaimani A, "Common pitfalls and mistakes in the set-up, analysis and interpretation of results in network meta-analysis: what clinicians should look for in a published article", Evid Based Ment Health, 20, pp.88~94 (2017)
- Higgins JP, "Consistency and inconsistency in network meta-analysis : concepts and models for multi-arm studies", Res Synth Methods, 3, pp.98~110 (2012)