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Related Concept Videos

Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...
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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Updated: Jun 2, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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SMART-MC: Characterizing the Dynamics of Multiple Sclerosis Therapy Transitions Using a Covariate-Based Markov Model.

Beomchang Kim1, Zongqi Xia2, Priyam Das1,3

  • 1Department of Biostatistics, Virginia Commonwealth University.

Journal of the American Statistical Association
|June 1, 2026
PubMed
Summary

Patient characteristics significantly influence treatment switching in Multiple Sclerosis (MS). Our novel SMART-MC model reveals how age and race impact transitions between disease-modifying therapies (DMTs).

Keywords:
Dynamic treatment modelingEHR data modelingGlobal optimizationMarkov modelMultiple Sclerosis

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Area of Science:

  • Biostatistics
  • Neurology
  • Health Services Research

Background:

  • Treatment switching is common in Multiple Sclerosis (MS) management.
  • Transitions occur due to varied responses, disease progression, patient factors, and adverse effects.

Purpose of the Study:

  • To investigate how patient-level covariates affect the likelihood of treatment transitions among disease-modifying therapies (DMTs) in MS.
  • To develop and validate a novel statistical framework for modeling these transitions.

Main Methods:

  • Adopted a Markovian framework, Sparse Matrix Estimation with Covariate-Based Transitions in Markov Chain Modeling (SMART-MC).
  • Modeled transition probabilities as functions of covariates, addressing identifiability with L2 norm constraints.
  • Developed a scalable, parallelized global optimization routine for likelihood function optimization.

Main Results:

  • Identified meaningful patterns in DMT transitions within MS patient subgroups.
  • Revealed variations in treatment switching based on age, race, and other clinical factors.
  • The SMART-MC model effectively handles sparse transitions and maintains interpretability.

Conclusions:

  • Patient covariates play a crucial role in DMT switching decisions for Multiple Sclerosis.
  • The SMART-MC framework provides an interpretable and efficient method for analyzing real-world treatment transitions.
  • Understanding these patterns can inform personalized MS treatment strategies.