An Innovative Approach to Modelling the Optimal Treatment Sequence for Patients with Relapsing-Remitting Multiple...

Area of Science:

• Computational modeling in healthcare
• Pharmacoeconomics and outcomes research
• Neurology and clinical trials

Background:

• Evaluating optimal treatment sequences for relapsing-remitting multiple sclerosis (RRMS) is complex.
• Disease-modifying therapies (DMTs) offer various efficacy and safety profiles.
• Personalized treatment strategies are crucial for managing RRMS.

Purpose of the Study:

• To develop and validate an innovative computational model for assessing DMT sequences in RRMS.
• To simulate patient trajectories and compare different decision-making approaches for treatment selection.
• To determine the impact of various criteria on optimal DMT sequencing and patient outcomes.

Main Methods:

• A patient-level discrete event simulation (DES) coupled with a Markov model was employed.
• The model incorporated heterogeneity in disease progression and patient outcomes.
• Internal and external validation confirmed the model's robustness and accuracy against published data.

Main Results:

• Model outcomes for natural disease history aligned with input parameters.
• Cost and quality-of-life results were validated against reference models.
• Different decision criteria yielded distinct optimal treatment sequences, with cladribine tablets consistently appearing across all scenarios.

Conclusions:

• The validated computational model accurately simulates RRMS patient trajectories.
• The framework aids in identifying optimal DMT sequences based on diverse clinical and economic criteria.
• This approach offers valuable insights for treatment switching and positioning decisions in RRMS management.