TY - JOUR
T1 - Predicting disability progression in multiple sclerosis
T2 - Insights from advanced statistical modeling
AU - Pellegrini, Fabio
AU - Copetti, Massimiliano
AU - Sormani, Maria Pia
AU - Bovis, Francesca
AU - de Moor, Carl
AU - Debray, Thomas Pa
AU - Kieseier, Bernd C
N1 - Funding Information:
Inconsistency of prognostic factor importance ranking confirmed the relatively poor prediction ability of baseline factors in modeling disease progression in MS. Our findings underline the importance to explore alternative predictors as well as alternative definitions of commonly used endpoints. Prognostic factor ranking pooled placebo arms MS disease progression advanced methods random forests model performance Biogen https://doi.org/10.13039/100005614 edited-state corrected-proof Excel Scientific Solutions copyedited and styled the manuscript per journal requirements. Biogen reviewed and provided feedback on the manuscript. The authors had full editorial control of the manuscript and provided their final approval of all content. Declaration of Conflicting Interests The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: F.P., C.d.M., and B.C.K. are employees of and hold stock/stock options in Biogen. M.C. has received consulting fees from Biogen, Eisai, and Teva. M.P.S. has received consulting fees from Biogen, GeNeuro, Genzyme, MedDay, Merck Serono, Novartis, Roche, and Teva. F.B. has received consulting fees from Biogen. T.P.A.D. has received consulting fees from Biogen. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was sponsored by Biogen (Cambridge, MA, USA) through funding for medical writing support in the development of this paper. ORCID iDs Massimiliano Copetti https://orcid.org/0000-0002-7960-5947 Maria Pia Sormani https://orcid.org/0000-0001-6892-104X Thomas PA Debray https://orcid.org/0000-0002-1790-2719 Supplemental Material Supplemental material for this article is available online.
Publisher Copyright:
© The Author(s), 2019.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - BACKGROUND: There is an unmet need for precise methods estimating disease prognosis in multiple sclerosis (MS).OBJECTIVE: Using advanced statistical modeling, we assessed the prognostic value of various clinical measures for disability progression.METHODS: Advanced models to assess baseline prognostic factors for disability progression over 2 years were applied to a pooled sample of patients from placebo arms in four different phase III clinical trials. least absolute shrinkage and selection operator (LASSO) and ridge regression, elastic nets, support vector machines, and unconditional and conditional random forests were applied to model time to clinical disability progression confirmed at 24 weeks. Sensitivity analyses for different definitions of a combined endpoint were carried out, and bootstrap was used to assess prediction model performance.RESULTS: A total of 1582 patients were included, of which 434 (27.4%) had disability progression in a combined endpoint over 2 years. Overall model discrimination performance was relatively poor (all C-indices ⩽ 0.65) across all models and across different definitions of progression.CONCLUSION: Inconsistency of prognostic factor importance ranking confirmed the relatively poor prediction ability of baseline factors in modeling disease progression in MS. Our findings underline the importance to explore alternative predictors as well as alternative definitions of commonly used endpoints.
AB - BACKGROUND: There is an unmet need for precise methods estimating disease prognosis in multiple sclerosis (MS).OBJECTIVE: Using advanced statistical modeling, we assessed the prognostic value of various clinical measures for disability progression.METHODS: Advanced models to assess baseline prognostic factors for disability progression over 2 years were applied to a pooled sample of patients from placebo arms in four different phase III clinical trials. least absolute shrinkage and selection operator (LASSO) and ridge regression, elastic nets, support vector machines, and unconditional and conditional random forests were applied to model time to clinical disability progression confirmed at 24 weeks. Sensitivity analyses for different definitions of a combined endpoint were carried out, and bootstrap was used to assess prediction model performance.RESULTS: A total of 1582 patients were included, of which 434 (27.4%) had disability progression in a combined endpoint over 2 years. Overall model discrimination performance was relatively poor (all C-indices ⩽ 0.65) across all models and across different definitions of progression.CONCLUSION: Inconsistency of prognostic factor importance ranking confirmed the relatively poor prediction ability of baseline factors in modeling disease progression in MS. Our findings underline the importance to explore alternative predictors as well as alternative definitions of commonly used endpoints.
KW - Prognostic factor ranking
KW - pooled placebo arms
KW - MS disease progression
KW - advanced methods
KW - random forests
KW - model performance
UR - http://www.scopus.com/inward/record.url?scp=85074815497&partnerID=8YFLogxK
U2 - 10.1177/1352458519887343
DO - 10.1177/1352458519887343
M3 - Article
C2 - 31686590
SN - 1352-4585
VL - 26
SP - 1828
EP - 1836
JO - Multiple Sclerosis Journal
JF - Multiple Sclerosis Journal
IS - 14
ER -