Efficient designs of multiple sclerosis clinical trials

Multiple sclerosis (MS) is a debilitating disease that attacks the central nervous system. Much research has been conducted to investigate the efficacy of various treatments in reducing the number of active brain lesions in patients, an indicator of disease activity. However, there has been little research regarding the time series nature of these lesion counts. This project focuses on sample size recommendations for Phase II MS/MRI clinical trials using a longitudinal model. We explore design recommendations based on two estimators. One is based on summary statistics, while the other, $\widehat{T}_{ML}$, uses the time series nature of lesion counts. $\widehat{T}_{ML}$ was found to provide robust sample size recommendations and, over sample size ranges found commonly in current Phase II MS/MRI clinical trials, was a substantial improvement over $\widehat{T}_{POST}$ in terms of sensitivity We further demonstrated that hypothesis tests based on T-hat-post are very powerful even for modest sample sizes.