Evidence map›Paper›PMID 42221835›Full record

ArticleJournal of the American Statistical Association2026

SMART-MC: Characterizing the Dynamics of Multiple Sclerosis Therapy Transitions Using a Covariate-Based Markov Model.

Beomchang Kim, Zongqi Xia, Priyam Das

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. A latent transition analysis of longitudinal stability of post-stroke fatigue.Social psychiatry and psychiatric epidemiology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Beomchang KimDepartment of Biostatistics, Virginia Commonwealth University.
Zongqi XiaDepartment of Neurology, Department of Biomedical Informatics, University of Pittsburgh.
Priyam DasDepartment of Biostatistics, Virginia Commonwealth University.

Funding

Leveraging electronic health records to optimize treatment selection and response in multiple sclerosisR01NS098023 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Zongqi Xia · 2016 to 2026
$4.6M
Real-world impact of the COVID-19 pandemic in people with multiple sclerosisR01NS124882 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI XIA, ZONGQI · 2022 to 2024
$1.2M
NINDS NIH HHS R01 NS098023NINDS NIH HHS R01 NS124882
6 · The paper itself

Abstract

Treatment switching is a common occurrence in the management of Multiple Sclerosis (MS), where patients transition across various disease-modifying therapies (DMTs) due to heterogeneous treatment responses, differences in disease progression, patient characteristics, and therapy-associated adverse effects. To investigate how patient-level covariates influence the likelihood of treatment transitions among DMTs, we adopt a Markovian framework,

Indexed as

Dynamic treatment modelingEHR data modelingGlobal optimizationMarkov modelMultiple Sclerosis

Identifiers

PMID42221835
PMCPMC13218670

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.