Harnessing real-world data to inform treatment decisions in multiple sclerosis

Leveraging Real-World Evidence and Observational Studies in Treating Multiple Sclerosis

Randomized controlled trials (RCTs) are essential for regulatory approval of disease-modifying therapies (DMTs), yet their strict selection criteria often lead to limited generalizability. Observational studies using real-world data (RWD) allow for more inclusive heterogeneous cohorts resulting in higher external validity to inform treatment practices. As reviewed in this article, well-designed comparative effectiveness studies are an important application of RWD. Although, like RCTs, observational studies have their own set of limitations, including various biases that may confound results, advanced statistical methods can mitigate many of these limitations. A focus on personalized treatment will continue to add value to individualize MS care.

Recommendations for the use of propensity score methods in multiple sclerosis research

BACKGROUND

With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies.

OBJECTIVES

To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research.

METHODS

We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice.

RESULTS

Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses.

CONCLUSION

This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods.

Dimethyl fumarate vs Teriflunomide: an Italian time-to-event data analysis

BACKGROUND

The introduction of oral disease-modifying therapies (DMTs) for relapsing-remitting multiple sclerosis (RRMS) changed algorithms of RRMS treatment.

OBJECTIVES

To compare the effectiveness of treatment with dimethyl fumarate (DMF) and teriflunomide (TRF) in a large multicentre Italian cohort of RRMS patients.

MATERIALS AND METHODS

Patients with RRMS who received treatment with DMF and TRF between January 1st, 2012 and December 31st, 2018 from twelve MS centers were identified. The events investigated were "time-to-first-relapse", "time-to-Magnetic-Resonance-Imaging (MRI)-activity" and "time-to-disability-progression".

RESULTS

1445 patients were enrolled (1039 on DMF, 406 on TRF) and followed for a median of 34 months. Patients on TRF were older (43.5 ± 8.6 vs 38.8 ± 9.2 years), with a predominance of men and higher level of disability (p < 0.001 for all). Patients on DMF had a higher number of relapses and radiological activity (p < .05) at baseline. Time-varying Cox-model for the event "time-to-first relapse" revealed that no differences were found between the two groups in the first 38 months of treatment (HR_t < 38DMF = 0.73, CI = 0.52 to 1.03, p = 0.079). When the time-on-therapy exceeds 38 months patients on DMF had an approximately 0.3 times lower relapse hazard risk than those who took TRF (HR_t>38DMF = 3.83, CI = 1.11 to 13.23, p = 0.033). Both DMTs controlled similarly MRI activity and disability progression.

CONCLUSIONS

Patients on DMF had higher relapse-free survival time than TRF group after the first 38 months on therapy.