Related Experiment Video
Updated: Jul 17, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Regression to the mean in multiple sclerosis
S Martínez-Yélamos1, A Martínez-Yélamos, G Martín Ozaeta
1Multiple Sclerosis Unit, Service of Neurology, Hospital Universitari de Bellvitge, IDIBELL, l'Hospitalet de Llobregat, Barcelona 08907, Spain.
Abstract:
In order to ensure sufficient disease activity, patients with relapsing remitting (RR) multiple sclerosis (MS) are often included in randomized placebo-controlled trials, only if they have a high baseline activity. These patients, whose evolution is unusual in the pre-study period, will tend to show a more usual behavior when followed up over a period of time. This phenomenon is known as regression to the mean. Regression to the mean should be taken into account in correctly interpreting long-term studies of cohorts treated without a placebo control group, which use the baseline period as control. The aim of this study was to evaluate the relevance of this phenomenon in a non-treated cohort of RRMS patients, selected with similar criteria to those used in randomized placebo-controlled clinical trials. Forty-four patients with definite RRMS, with two or more relapses in the previous two years, and a baseline EDSS < or = 5.5 were prospectively followed. The mean number of relapses spontaneously decreased from 1.72 (SD: 1.4) in the year prior to enrolment, to 1.0 (SD: 1.3) during the first year of follow-up (P < 0.05). Regression to the mean may explain as much as 40% of the reduction in the relapse rate from the baseline period to the period on-study.
Related Concept Videos
Regression Toward the Mean
Multiple Sclerosis l: Introduction
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

