Disease modeling in multiple sclerosis: Assessment and quantification of sources of variability in brain parenchymal fraction measurements

In this work, we propose a new model for estimating variability in serial morphometry, particularly the BPF measurement. Specifically, we attempt to detect and explicitly model the remaining sources of error to more accurately describe the overall variability in BPF measurements. Our results show that sources of variability beyond subject repositioning error are important and cannot be ignored. We demonstrate that scan-rescan experiments only provide a lower bound on the true error in repeated measurements of patients' BPF. We have estimated the variance due to patient repositioning during scan-rescan (σsr2 = 3.0e-06), variance assigned to physiological fluctuations (σp2 = 5.74e-06) and the variance associated with lesion activity (σles2 = 1.09e-05). These variance components can be used to determine the relative impact of their sources on sample size estimates for studies investigating change over time in MS patients. Our results demonstrate that sample size calculations based exclusively on scan-rescan variability (σsr) are likely to underestimate the number of patients required. If the physiological variability (σp) is incorporated in sample size calculations, the required sample size would increase by a factor of 5.69 based on standard t-test sample size calculation.