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New Developments of Minimum Clinically Important Difference: Theory and Methodology

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Title: New Developments of Minimum Clinically Important Difference: Theory and Methodology
Author(s): Xu, Tu
Advisor(s): Hedayat, Samad; Wang, Junhui
Contributor(s): Wang, Junhui; Wang, Jing; Martin, Ryan; Chen, Huayun
Department / Program: Mathematics, Statistics, and Computer Science
Graduate Major: Mathematics
Degree Granting Institution: University of Illinois at Chicago
Degree: PhD, Doctor of Philosophy
Genre: Doctoral
Subject(s): Fisher consistency large margin classification minimum clinically important difference non-convex minimization support vector machine
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. In this dissertation, a new estimation framework of MCID using both diagnostic measurements and patient-reported outcomes (PROs) is proposed. 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, it is shown 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 real applications.
Issue Date: 2014-02-24
Genre: thesis
Rights Information: Copyright 2013 Tu Xu
Date Available in INDIGO: 2014-02-24
Date Deposited: 2013-12

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