Abstract
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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