Minimum clinically important difference in medical studies.

In clinical trials, minimum clinically important difference (MCID) has attracted increasing interest as an important supportive clinical and statistical inference tool. Many estimation methods have been developed based on various intuitions, while little theoretical justification has been established. This article proposes a new estimation framework of the MCID using both diagnostic measurements and patient-reported outcomes (PROs). The framework first formulates the population-based MCID as a large margin classification problem, and then extends to the personalized MCID to allow individualized thresholding value for patients whose clinical profiles may affect their PRO responses. More importantly, the proposed estimation framework is showed to be asymptotically consistent, and a finite-sample upper bound is established for its prediction accuracy compared against the ideal MCID. The advantage of our proposed method is also demonstrated in a variety of simulated experiments as well as two phase-3 clinical trials.