  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.
