The minimal clinically important difference (MCID) is the smallest change in a treatment outcome that an individual patient would identify as important. In the era of precision medicine, it is of particular interest to study both point and interval estimations for the individualized MCID. The motivating example of this work is the ChAMP trial, which is a randomized controlled trial to compare debridement to observation of chondral lesions encountered during partial meniscectomy. In this trial, the primary outcome is the patient reported pain score one year after the surgery and we are interested in estimating the individualized MCID so that the treatment effect can be further studied. In this paper, we formulize this problem in a classification setting where nonconvex minimization technique is needed for the optimization. Furthermore, we develop the Bahadur representation of the individualized MCID so that its confidence interval can be derived. The proposed method is illustrated via comprehensive simulation studies. We also apply our proposed methodology to the ChAMP trial analysis.