Detection of Unusual Increases in MRI Lesion Counts in Individual Multiple Sclerosis Patients

Abstract

Data Safety and Monitoring Boards (DSMBs) for multiple sclerosis clinical trials consider an increase of contrast-enhancing lesions on repeated magnetic resonance imaging an indicator for potential adverse events. However, there are no published studies that clearly identify what should be considered an “unexpected increase” of lesion activity for a patient. To address this problem, we consider as an index the likelihood of observing lesion counts as large as those observed on the recent scans of a patient conditional on the patient’s lesion counts on previous scans. To estimate this index, we rely on random effects models. Given the patient-specific random effect, we assume that the repeated lesion counts from the same patient follow a negative binomial distribution and may be correlated over time. We fit the model using data collected from the trial under DSMB review and update the estimation when new data are to be reviewed. We consider two estimation procedures: maximum likelihood for a fully parameterized model and a simple semiparametric method for a model with an unspecified distribution for the random effects. We examine the performance of our methods using simulations and illustrate the approach using data from a clinical trial. Supplementary materials for this article are available online.