Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling.

Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling.

Publication date: Nov 05, 2019

There is an unmet need for precise methods estimating disease prognosis in multiple sclerosis (MS).

Using advanced statistical modeling, we assessed the prognostic value of various clinical measures for disability progression.

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.

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.

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.

Pellegrini, F., Copetti, M., Sormani, M.P., Bovis, F., de Moor, C., Debray, T.P., and Kieseier, B.C. Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling. 19444. 2019 Mult Scler.

Concepts Keywords
Bootstrap MS
Conditional Health
Disability Clinical research
Discrimination Medical statistics
Elastic Clinical pharmacology
LASSO Clinical trial
Multiple Sclerosis Design of experiments
Operator Pharmaceutical industry
Placebo Medical research
Prognosis
Random Forests
Ridge Regression
Statistical Modeling
Vector

Semantics

Type Source Name
disease MESH multiple sclerosis
disease DOID multiple sclerosis
gene UNIPROT KIDINS220
disease MESH disease progression

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