Record Status This is a bibliographic record of a published health technology assessment from a member of INAHTA. No evaluation of the quality of this assessment has been made for the HTA database. Citation Tilling K, Lawton M, Robertson N, Tremlett H, Zhu F, Harding K, Oger J, Ben-Shlomo Y. Modelling disease progression in relapsing-remitting onset multiple sclerosis using multilevel models applied to longitudinal data from two natural history cohorts and one treated cohort. Health Technology Assessment 2016; 20(81) Authors' objectives The ability to better predict disease progression represents a major unmet need in multiple sclerosis (MS), and would help to inform therapeutic and management choices.
This study aims to develop multilevel models using longitudinal data on disease progression in patients with relapsing–remitting MS (RRMS) or secondary-progressive MS (SPMS); and to use these models to estimate the association of disease-modifying therapy (DMT) with progression.
Authors' conclusions EDSS score progression in two natural history cohorts of MS patients showed a similar pattern. Progression in the natural history cohorts was slightly faster than EDSS score progression in the DMT-treated cohort, up to 6 years post treatment. Indexing Status Subject indexing assigned by CRD MeSH Disease Progression; Humans; Multilevel Analysis; Multiple Sclerosis; Multiple Sclerosis, Relapsing-Remitting Language Published English Country of organisation England English summary An English language summary is available. Address for correspondence NETSCC, Health Technology Assessment, Alpha House, University of Southampton Science Park, Southampton, SO16 7NS UK Tel: +44 23 8059 5586 Email: hta@hta.ac.uk AccessionNumber 32016001092 Date abstract record published 04/11/2016 |