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Early prediction of the long term evolution of multiple sclerosis: the Bayesian Risk Estimate for Multiple Sclerosis
Roberto Bergamaschi1, Silvana Quaglini, Maria Trojano
1Multiple Sclerosis Centre, Department of Clinical Neurology, Neurological Institute C Mondino, Via Mondino 2, 27100 Pavia, Italy. roberto.bergamaschi@mondino.it
Aim:
To propose a simple tool for early prediction of unfavourable long term evolution of multiple sclerosis (MS).
Methods:
A Bayesian model allowed us to calculate, within the first year of disease and for each patient, the Bayesian Risk Estimate for MS (BREMS) score that represents the risk of reaching secondary progression (SP).
Results:
The median BREMS scores were higher in 158 patients who reached SP within 10 years compared with 1087 progression free patients (0.69 vs 0.30; p<0.0001). The BREMS value was related to SP risk in the whole cohort (p<0.0001) and in the subgroup of 535 patients who had never been treated with immune therapies, thus reasonably representing the natural history of the disease (p<0.000001).
Conclusions:
The BREMS score may be useful both to identify patients who are candidates for early or for more aggressive therapies and to improve the design and analysis of clinical therapeutic trials and of observational studies.
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However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...