Treating Systematic Errors in Multiple Sclerosis Data

Multiple sclerosis (MS) is characterized by high variability
between patients and, more importantly here, within an individual
over time. This makes categorization and prognosis difficult.
Moreover, it is unclear to what degree this intra-individual
variation reflects the long-term course of irreversible disability
and what is attributable to short-term processes such as relapses,
to interrater variability and to measurement error. Any
investigation and prediction of the medium or long term evolution
of irreversible disability in individual patients is therefore
confronted with the problem of systematic error in addition to
random fluctuations. The approach described in this article aims to
assist in detecting relapses in disease curves and in identifying
the underlying disease course. To this end neurological knowledge
was transformed into simple rules which were then implemented into
computer algorithms for pre-editing disease curves. Based on
simulations it is shown that pre-editing time series of disability
measured with the Expanded Disability Status Scale (EDSS) can lead
to more robust and less biased estimates for important disease
characteristics, such as baseline EDSS and time to reach certain
EDSS levels or sustained progression.