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J Neurol Sci. 2011 Apr 15;303(1-2):109-13. doi: 10.1016/j.jns.2010.12.024. Epub 2011 Jan 19.

Accounting for disease modifying therapy in models of clinical progression in multiple sclerosis.

Author information

1
Department of Neurology, Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Harvard Medical School, Brookline, MA 02115, USA. bchealy@partners.org

Abstract

Identifying predictors of clinical progression in patients with relapsing-remitting multiple sclerosis (RRMS) is complicated in the era of disease modifying therapy (DMT) because patients follow many different DMT regimens. To investigate predictors of progression in a treated RRMS sample, a cohort of RRMS patients was prospectively followed in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB). Enrollment criteria were exposure to either interferon-β (IFN-β, n=164) or glatiramer acetate (GA, n=114) for at least 6 months prior to study entry. Baseline demographic and clinical features were used as candidate predictors of longitudinal clinical change on the Expanded Disability Status Scale (EDSS). We compared three approaches to account for DMT effects in statistical modeling. In all approaches, we analyzed all patients together and stratified based on baseline DMT. Model 1 used all available longitudinal EDSS scores, even those after on-study DMT changes. Model 2 used only clinical observations prior to changing DMT. Model 3 used causal statistical models to identify predictors of clinical change. When all patients were considered using Model 1, patients with a motor symptom as the first relapse had significantly larger change in EDSS scores during follow-up (p=0.04); none of the other clinical or demographic variables significantly predicted change. In Models 2 and 3, results were generally unchanged. DMT modeling choice had a modest impact on the variables classified as predictors of EDSS score change. Importantly, however, interpretation of these predictors is dependent upon modeling choice.

PMID:
21251671
DOI:
10.1016/j.jns.2010.12.024
[Indexed for MEDLINE]

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