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 assessment quality of the longitudinal study. RC estimation accuracy depends on the case number in the test-retest study, but despite its pivotal role, no comprehensive framework for sample size calculation of test-retest studies exists. To address this issue, we have established such a framework, which allows for flexible sample size calculation of test-retest studies, based upon newly introduced criteria concerning assessment quality in the longitudinal study. This also permits retrospective assessment of prior test-retest studies.

翻译：定量生物标志物的数量不断增加，这些标志物有望实现对治疗反应或疾病进展的早期评估。然而，定量生物标志物的测量结果存在随机变异性。因此，纵向测量中生物标志物的差异不一定代表真实变化，而可能由这种随机测量变异引起。在纵向研究中使用定量生物标志物之前，评估其测量可重复性至关重要。通过测试-重测研究获得的测量可重复性可用可重复性系数（RC）来量化，该系数随后用于后续纵向研究，以确定测量的差异是代表真实变化，还是属于预期随机测量变异范围。因此，RC点估计的质量直接决定了纵向研究的评估质量。RC估计精度取决于测试-重测研究中的样本数量，但尽管其作用关键，目前尚无针对测试-重测研究样本量计算综合框架。为解决此问题，我们建立了这样一个框架，基于纵向研究中关于评估质量的新引入标准，可灵活计算测试-重测研究的样本量，并同时允许对先前的测试-重测研究进行回顾性评估。