Literature DB >> 20936625

The role of the minimum clinically important difference and its impact on designing a trial.

Christy Chuang-Stein1, Simon Kirby, Ian Hirsch, Gary Atkinson.   

Abstract

The minimum clinically important difference (MCID) between treatments is recognized as a key concept in the design and interpretation of results from a clinical trial. Yet even assuming such a difference can be derived, it is not necessarily clear how it should be used. In this paper, we consider three possible roles for the MCID. They are: (1) using the MCID to determine the required sample size so that the trial has a pre-specified statistical power to conclude a significant treatment effect when the treatment effect is equal to the MCID; (2) requiring with high probability, the observed treatment effect in a trial, in addition to being statistically significant, to be at least as large as the MCID; (3) demonstrating via hypothesis testing that the effect of the new treatment is at least as large as the MCID. We will examine the implications of the three different possible roles of the MCID on sample size, expectations of a new treatment, and the chance for a successful trial. We also give our opinion on how the MCID should generally be used in the design and interpretation of results from a clinical trial.
Copyright © 2010 John Wiley & Sons, Ltd.

Mesh:

Year:  2010        PMID: 20936625     DOI: 10.1002/pst.459

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  9 in total

Review 1.  Recommendations for planning pilot studies in clinical and translational research.

Authors:  Charity G Moore; Rickey E Carter; Paul J Nietert; Paul W Stewart
Journal:  Clin Transl Sci       Date:  2011-10       Impact factor: 4.689

Review 2.  Clinical Benefit Scales and Trial Design: Some Statistical Issues.

Authors:  Edward L Korn; Carmen J Allegra; Boris Freidlin
Journal:  J Natl Cancer Inst       Date:  2022-09-09       Impact factor: 11.816

3.  Value of Information Analysis in Models to Inform Health Policy.

Authors:  Christopher H Jackson; Gianluca Baio; Anna Heath; Mark Strong; Nicky J Welton; Edward C F Wilson
Journal:  Annu Rev Stat Appl       Date:  2022-03-07       Impact factor: 7.917

4.  A Review of Bayesian Perspectives on Sample Size Derivation for Confirmatory Trials.

Authors:  Kevin Kunzmann; Michael J Grayling; Kim May Lee; David S Robertson; Kaspar Rufibach; James M S Wason
Journal:  Am Stat       Date:  2021-04-22       Impact factor: 8.710

5.  Optimizing the development and evaluation of complex interventions: lessons learned from the BetterBirth Program and associated trial.

Authors:  Dale A Barnhart; Katherine E A Semrau; Corwin M Zigler; Rose L Molina; Megan Marx Delaney; Lisa R Hirschhorn; Donna Spiegelman
Journal:  Implement Sci Commun       Date:  2020-02-25

6.  The minimal clinically important difference after simple decompression for ulnar neuropathy at the elbow.

Authors:  Sunitha Malay; Kevin C Chung
Journal:  J Hand Surg Am       Date:  2013-03-06       Impact factor: 2.230

7.  Utilization of brief pain inventory as an assessment tool for pain in patients with cancer: a focused review.

Authors:  Senthil P Kumar
Journal:  Indian J Palliat Care       Date:  2011-05

8.  The Effectiveness of Pharmacopuncture in Patients with Lumbar Spinal Stenosis: A Protocol for a Multi-Centered, Pragmatic, Randomized, Controlled, Parallel Group Study.

Authors:  Jee Young Lee; Kyoung Sun Park; Suna Kim; Ji Yeon Seo; Hyun-Woo Cho; Dongwoo Nam; Yeoncheol Park; Eun-Jung Kim; Yoon Jae Lee; In-Hyuk Ha
Journal:  J Pain Res       Date:  2022-09-23       Impact factor: 2.832

Review 9.  Sample size calculations in pediatric clinical trials conducted in an ICU: a systematic review.

Authors:  Stavros Nikolakopoulos; Kit C B Roes; Johanna H van der Lee; Ingeborg van der Tweel
Journal:  Trials       Date:  2014-07-08       Impact factor: 2.279

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.