Over the past two decades, an ever-expanding range of disease-modifying therapies
(DMTs) has become available for multiple sclerosis (MS) on the basis of their efficacy
in randomized controlled trials (RCTs). At the same time, the growing number of new
DMTs has made treatment decisions more complicated in clinical practice. Major questions
remain concerning long-term risks and benefits of therapies, risk stratification,
treatment sequencing, benefits of different dosing and routes of administration and
outcome definition. Post-marketing studies based on real-world (RW) data has gained
attention as a good strategy for capturing important additional information to complement
data from RCTs in order to address issues around the uncertainty of the evidence base.
In the last years, the increasing amount of RW data collected in MS registries, administrative
databases and health electronic records, the evolution in information technology infrastructure
and the corresponding ability to store and process these data through new and more
sophisticated statistical methods, drove an exponential growth of high quality post-marketing
studies of MS treatments. These studies are providing insights into predictors of
safety, tolerability and effectiveness of DMTs and the long-term comparative effectiveness
between different DMTs that are proving to be critical in directing and improving
MS intervention strategies in daily practice. Moreover post-marketing research, has
become an integral part of the drug evaluation process for a wide range of agents.
Regulatory bodies and pharma companies have stated their ambition for greater use
of RWD for treatment decision making, post-approval surveillance studies and pricing
and reimbursement decisions.
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