ArticleJAMA network open2021
Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis.
Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Rituximab for people with multiple sclerosis.The Cochrane database of systematic reviews · 2025Pooled it
- Knowledge graph-guided multiple sclerosis identification and therapeutic trend analysis: Real-world evidence from two large healthcare systems.PLOS digital health · 2026Article
- Leveraging electronic health records to examine differential clinical outcomes in people with Alzheimer's disease.Communications medicine · 2026Article
- Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response.Journal of machine learning research : JMLR · 2025Article
- Article
- A comparison of clinical, utilization, and cost outcomes between oral treatments for multiple sclerosis.Journal of managed care & specialty pharmacy · 2024Article
- LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.Patterns (New York, N.Y.) · 2024Article
- Clustering Sequence Data with Mixture Markov Chains with Covariates Using Multiple Simplex Constrained Optimization Routine (MSiCOR).Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Association between serum multi-protein biomarker profile and real-world disability in multiple sclerosis.Brain communications · 2024Article
- Comparison of Pharmacological Therapies in Relapse Rates in Patients With Relapsing-Remitting Multiple Sclerosis.Cureus · 2023Review
- Clinical and economic evaluations of natalizumab, rituximab, and ocrelizumab for the management of relapsing-remitting multiple sclerosis in Saudi Arabia.BMC health services research · 2023Article
- Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.Journal of medical Internet research · 2023Article
- Treatment of multiple sclerosis with rituximab: A Spanish multicenter experience.Frontiers in neurology · 2023Article
- Patterns of Utilization and Expenditure Across Multiple Sclerosis Disease-Modifying Therapies: A Retrospective Cohort Study Using Claims Data from a Commercially Insured Population in the United States, 2010-2019.Neurology and therapy · 2022Article
- Effect of Rituximab Compared with Natalizumab and Fingolimod in Patients with Relapsing-Remitting Multiple Sclerosis: A Cohort Study.Journal of clinical medicine · 2022Article
- Therapeutic Advances in Multiple Sclerosis.Frontiers in neurology · 2022Review
- Comparative effectiveness of natalizumabTherapeutic advances in neurological disorders · 2022Article
- Temporal trends of multiple sclerosis disease activity: Electronic health records indicators.Multiple sclerosis and related disorders · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Importance: As disease-modifying treatment options for multiple sclerosis increase, comparisons of the options based on real-world evidence may guide clinical decision-making. Objective: To compare the relapse outcomes between 2 pairs of disease-modifying treatments: dimethyl fumarate vs fingolimod and natalizumab vs rituximab. Design, Setting, and Participants: This comparative effectiveness study integrated data from a clinic-based multiple sclerosis research registry and its linked electronic health records (EHR) system between January 1, 2006, and December 31, 2016, and built treatment groups for each pairwise disease-modifying treatment comparison according to both registry records and electronic prescriptions. Parallel analyses were conducted from October 11, 2019, to July 7, 2021. Main Outcomes and Measures: The main outcomes were the 1-year and 2-year relapse rates as well as the time to relapse. To compare relapse outcomes, the study adjusted for covariates from 2 sources (registry and EHR) and corrected for confounding biases among the covariates by the doubly robust estimation. Results: The study included 4 treatment groups: dimethyl fumarate (n = 260; 198 women [76.2%]; 227 non-Hispanic White individuals [87.3%]; mean [SD] age at diagnosis, 41.7 [10.4] years), fingolimod (n = 267; 190 women [71.2%]; 222 non-Hispanic White individuals [83.1%]; mean [SD] age at diagnosis, 37.9 [9.9] years), natalizumab (n = 204; 160 women [78.4%]; 172 non-Hispanic White individuals [84.3%]; mean [SD] age at diagnosis, 37.2 [10.6] years), and rituximab (n = 115; 83 women [72.2%]; 99 non-Hispanic White individuals [86.1%]; mean [SD] age at diagnosis, 44.1 [11.1] years). No significant differences were found in the relapse outcomes between dimethyl fumarate and fingolimod after correcting for confounding biases and multiple testing (difference in 1-year relapse rate, 0.028 [95% CI, -0.031 to 0.084]; difference in 2-year relapse rate, 0.071 [95% CI, 0.008-0.128]; relative risk of 2-year non-relapse, 0.957 [95% CI, 0.884-1.035] with dimethyl fumarate as reference). When compared with rituximab, natalizumab was associated with a higher relapse rate for all 3 outcomes after bias correction and multiple testing (difference in 1-year relapse rate, 0.080 [95% CI, 0.013-0.137]; difference in 2-year relapse rate, 0.132 [95% CI, 0.043-0.189]; relative risk of 2-year non-relapse, 0.903 [95% CI, 0.822-0.944]). Confounders were identified from EHR data not recorded in the registry data through data-driven feature selection. Conclusions and Relevance: This study reports real-world evidence of equivalent relapse outcomes between dimethyl fumarate and fingolimod and relapse reduction in favor of rituximab relative to natalizumab. This approach illustrates the value of incorporating EHR data as high-dimensional covariates in real-world treatment comparison.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.