A statistical framework for planning and analysing test-retest studies for repeatability of quantitative biomarker measurements
Moritz Fabian Danzer, Maria Eveslage, Dennis G\"orlich, Benjamin Noto

TL;DR
This paper introduces a statistical framework for calculating the necessary sample size in test-retest studies to accurately assess the repeatability of quantitative biomarkers, crucial for reliable longitudinal analysis.
Contribution
It provides a novel, flexible sample size calculation framework for test-retest studies, enhancing assessment quality and enabling retrospective evaluations.
Findings
Framework allows flexible sample size calculation
Enables retrospective assessment of previous studies
Improves reliability of biomarker repeatability estimates
Abstract
There is an increasing number of potential biomarkers that could allow for early assessment of treatment response or disease progression. However, measurements of quantitative biomarkers are subject to random variability. Hence, differences of a biomarker in longitudinal measurements do not necessarily represent real change but might be caused by this random measurement variability. Before utilizing a quantitative biomarker in longitudinal studies, it is therefore essential to assess the measurement repeatability. Measurement repeatability obtained from test-retest studies can be quantified by the repeatability coefficient (RC), which is then used in the subsequent longitudinal study to determine if a measured difference represents real change or is within the range of expected random measurement variability. The quality of the point estimate of RC therefore directly governs the…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Biosimilars and Bioanalytical Methods · Meta-analysis and systematic reviews
