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 li
ttle theoretical justification has been established. This paper proposes a new estimation framework of MCID using both diagnostic measurements and patient-reported outcomes (PROs). It first provides a precise definition of population-based MCID so that estimating such a MCID can be formulated as a large margin classification problem. The framework is then extended to personalized MCID to allow individualized thresholding value for patients whose clinical profiles may affect their PRO responses. More importantly, we show that the proposed estimation framework is 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 applications to two benchmark datasets and two phase-3 clinical trials.
