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Abstract #4153

Enhancing Analysis and Study Design of Medical Imaging Markers Through Measurement Error Modeling

Xiaofeng Wang1, Walter Zhao2, Yifan Wang1, Deborah H. Kwon3, Ting-Yu Su4, Zhong Irene Wang4, and Dan Ma2
1Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States, 2Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 33. Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, United States, 4Neurological Institute, Cleveland Clinic, Cleveland, OH, United States

Synopsis

Keywords: Data Processing, biomarkers

Motivation: Measurement error in quantitative imaging markers (QIMs) biases parameter estimation and reduces statistical power, limiting the reliability of clinical imaging studies.

Goal(s): Demonstrate the importance of modeling QIM measurement error using regression calibration to improve estimation of associations between QIMs and clinical outcomes.

Approach: We provide a framework for measurement error calibration in medical imaging studies, which we demonstrate in a clinical study for epilepsy seizure onset zone detection.

Results: Correcting for measurement error significantly improved the estimation of associations, enhanced statistical power, corrected bias in regression analyses, and informed appropriate sample size estimation for clinical imaging studies.

Impact: We demonstrate how measurement error modeling significantly enhances clinical imaging study design and analysis by improving parameter estimation, statistical power, and study reliability. Our methodology is particularly valuable for multicenter and longitudinal studies where measurement variability is a key concern.

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Keywords