The minimal clinically important difference (MCID) refers to the smallest change in treatment effect that is meaningful to patients and serves as a key metric for regulatory agencies in evaluating the clinical value of drugs or therapeutic interventions. Rooted in a patient-centered approach, the MCID not only reflects the magnitude of clinical improvement but also embodies patients' emphasis on the change. However, the applicability of various MCID estimation methods across clinical scenarios has not been systematically compared, leading to inconsistent methodological selection and significant variability in estimated results in clinical research. Previous studies have indicated that the selection of appropriate MCID methods should comprehensively consider characteristics such as sample size and treatment effect; inappropriate method selection across different scenarios may introduce significant bias into MCID estimates, thereby compromising the robustness of clinical research conclusions and the scientific validity of regulatory decision-making. Therefore, the appropriate selection of statistical methods is critical for obtaining accurate and reliable MCID estimates. This article systematically reviews commonly used MCID estimation methods in clinical research and evaluates the statistical performance of multiple anchor-based and distribution-based approaches through simulation experiments, providing a reasonable basis for MCID method selection.

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