Modeling lesion counts in multiple sclerosis when patients have been selected for baseline activity.

The number of new gadolinium-enhancing lesions discovered via magnetic resonance imaging is a well-established outcome for multiple sclerosis studies, especially Phase II Studies. Due to the high cost of magnetic resonance imaging scans, many investigators select participants for the presence of lesions. While this selection procedure is thought to improve the power of inferences, the effect of screening for baseline activity on parameter estimation and interval coverage has not yet been examined. The objective of this study was to investigate the performance of the negative binomial distribution for modeling lesion count data in multiple sclerosis when patients have been selected for activity on a baseline scan. We performed computer simulations to investigate the influence of the screening process on inferences made using a negative binomial model about treatment effects in two independent samples. We also demonstrate how the statistical properties of screening can be incorporated into trial design. We demonstrate that when the negative binomial distribution is used to model lesion counts, while screening for baseline activity improves point estimation, this practice also has the potential to decrease interval coverage and inflate the Type I error rate. For data that is to be modeled using a negative binomial distribution, screening for baseline activity can create a trade-off between cost effectiveness and a higher than desired false positive rate that must be carefully considered in planning Phase II trials.