On Minimum Clinically Important Difference

TL;DR

This paper introduces a new theoretical framework for estimating the minimum clinically important difference (MCID) in clinical trials, incorporating diagnostic and patient-reported data, with proven consistency and practical validation.

Contribution

It develops a novel estimation approach for population and personalized MCID, with theoretical guarantees and empirical validation on real and simulated data.

Findings

The proposed method is asymptotically consistent.

Finite-sample prediction bounds are established.

Demonstrated effectiveness on clinical trial datasets.

Abstract

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 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.