ISSN: 2574-7797
Authors: Nellesen D, Birnbaum HG and Greenberg PE*
Among the many challenges that must be overcome in successfully developing a novel drug treatment is to understand and prepare evidence that will ultimately be required by regulators and reimbursement authorities. Payers and other stakeholders often state the need for evidence of the comparative effectiveness of new treatments, in some cases arguing that this evidence should be a standard requirement for market access and reimbursement. Comparative effectiveness research (CER) is a method of developing evidence of the clinical and cost effectiveness of true alternative interventions, potentially including long-term data that assess patientcentered outcomes and evidence that a new treatment actually changes clinical practice. Conceiving of and executing a plan for developing CER to support these evidence requirements is therefore a substantial challenge, one made even more difficult by evolving notions of CER and uncertainty in what constitutes sufficient evidence of comparative effectiveness. In fact, CER as a methodology is rapidly evolving, incorporating new sources of real-world and big data and applying cutting edge analytic techniques including novel statistical methods and machine learning
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