Analysing the course of multiple sclerosis with segmented regression models

Multiple sclerosis (MS) is a demyelinating disease of the central
nervous system whose cause is still unknown. The disease course
shows great inter- and intra-individual variability and this
results in insecurity of diagnosis and prognosis. A well-founded
knowledge of the natural history of MS, however, is an important
prerequisite for developing adequate strategies for therapy and
research. In order to increase our understanding we developed a
segmented regression model which extracts three main
characteristics of the time course of this complex disease from
natural history data. For each individual patient this model
determines baseline disability (as measured by the Expanded
Disability Status Scale = EDSS), the time point where the disease
starts to progress and the slope of this progression. The model is
applied to data of patient registries from all over the world that
are pooled in the database of the Sylvia Lawry Centre for Multiple
Sclerosis Research (SLCMSR). The analyses used a random subsample
of the entire database and were restricted to patients seen from
onset of MS with time series of at least three years. Thereby we
were able to avoid some of the problems related to missing data.
Our results revealed a weak negative correlation between time to
progression (change point) and slope of progression for this group
of patients, i.e. those patients who do progressed later and
remained stable for a longer time developed disability more slowly
than those who progressed earlier. For the two parameters and their
interaction we did not find an influence of basic covariates like
gender, disease course and mono- or poly-symptomatic disease onset.
According to the SLCMSR Policy these results will be subjected to a
validation using an independent "validation dataset". This remains
to be done.