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.
%0 Journal Article
%1 ChuangStein2011
%A Chuang-Stein, Christy
%A Kirby, Simon
%A Hirsch, Ian
%A Atkinson, Gary
%D 2011
%J Pharmaceutical statistics
%K ClinicalTrialsasTopic ClinicalTrialsasTopic:statistics&numericald DataInterpretation Humans Models Probability ResearchDesign SampleSize Statistical Theoretical TreatmentOutcome
%N 3
%P 250-6
%R 10.1002/pst.459
%T The role of the minimum clinically important difference and its impact on designing a trial.
%U http://dx.doi.org/10.1002/pst.459 http://www.ncbi.nlm.nih.gov/pubmed/20936625
%V 10
%X 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.
%@ 1539-1612
@article{ChuangStein2011,
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.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Chuang-Stein, Christy and Kirby, Simon and Hirsch, Ian and Atkinson, Gary},
biburl = {https://www.bibsonomy.org/bibtex/26af695421cb0b3b35068eae94bce3e17/jepcastel},
doi = {10.1002/pst.459},
interhash = {fc7ea700fb6385956c10604d284096bb},
intrahash = {6af695421cb0b3b35068eae94bce3e17},
isbn = {1539-1612},
issn = {1539-1612},
journal = {Pharmaceutical statistics},
keywords = {ClinicalTrialsasTopic ClinicalTrialsasTopic:statistics&numericald DataInterpretation Humans Models Probability ResearchDesign SampleSize Statistical Theoretical TreatmentOutcome},
note = {6155<m:linebreak></m:linebreak>MDCI; Sample size; Tamaño del efecto},
number = 3,
pages = {250-6},
pmid = {20936625},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {The role of the minimum clinically important difference and its impact on designing a trial.},
url = {http://dx.doi.org/10.1002/pst.459 http://www.ncbi.nlm.nih.gov/pubmed/20936625},
volume = 10,
year = 2011
}