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Why does statistical significance not equal clinical importance? Learn how MCID reveals true treatment effects!
As clinical research progresses, the medical community gradually realizes that relying solely on statistical significance to judge treatment effects is not enough. In this ongoing discussion, the concept of minimal clinically important difference (MCID) emerged and became an important indicator for evaluating treatment effects. So why doesn’t statistical significance equal clinical importance? This is particularly important when we understand and apply MCID.
Statistical significance does not imply clinical importance.
The difference between statistical significance and clinical importance
Statistical significance is usually measured as a P value, with a P value less than 0.05 generally considered significant. However, a limitation of this metric is that it may mask truly meaningful changes for patients. Even if a treatment is statistically significant, the clinical significance of the treatment is questionable if the degree of change is much less than the change perceived by the patient.
A study may show statistical significance, but what does the result mean if the resulting changes are not clinically useful to patients?
The meaning and application of MCID
Minimum clinically important difference (MCID) is defined as the degree of change at the end of treatment that a patient agrees is important to them. Within this framework, MCID is a key reference metric for evaluating efficacy. Clinicians and researchers are increasingly shifting the focus from physical measures to patient-reported outcomes, a shift that allows us to better capture what patients are really experiencing.
Several methods to evaluate MCID
Different methods can be used to evaluate MCID, mainly divided into three categories: distribution-based methods, anchor-based methods, and Delphi methods.
Distribution-based method
This method relies primarily on statistical measures such as standard deviation, standard error, and effect size, often described using standardized mean differences. Use "half a standard deviation" as a benchmark to evaluate whether patients have reached a clinically important difference.
Anchor-based method
The anchoring method compares the patient's changed score to an "anchor point." This means that patients need to assess, based on their own experience, how they feel after treatment and whether there is sufficient improvement. This approach is more individualized, but there is currently no consensus on the optimal question design.
Delphi method
This approach relies on expert panel consensus to assess the MCID of a treatment. Experts will independently evaluate the trial results and then reach a consensus through multiple feedbacks and revisions.
Challenges and flaws of MCID
Although MCID can provide an important reference indicator for medical decision-making, in some cases, anchor-based methods may not be suitable. It is difficult to clearly define "clinically significant improvement" when most patients improve after treatment. Furthermore, different diseases and outcome measurement tools result in different MCID values, complicating comparisons between different behaviors.
In medical research, MCID has become a key indicator for understanding treatment effects, but its uncertainties and challenges have also brought tests to clinical practice.
Conclusion
As medicine advances, how to better measure and understand patients' feelings has become an increasingly important issue. Statistical significance is important, but what needs more attention is the clinical importance behind it. Not only does MCID help us gain a clearer understanding of how treatments actually work, it also forces us to be more humane in our medical practice. But when faced with the gap between statistical significance and clinical significance, we should think about which changes are really worthy of our attention?
