A New Landscape for Comparative Effectiveness Research
There is a clear movement toward a more pragmatic application of real-world data to meet the needs of both regulators and payers.
Over the past decade, there have been signs that the landscape for comparative effectiveness research (CER) is shifting. When the idea of CER – that is, evidence on the effectiveness and consequences of different treatment options that would inform decision making – was conceived nearly a decade ago, it was framed in a context where the availability of real-world data and methods for analysis were relatively limited. Accordingly, clinical trials played a necessary and central role in assessing comparative effectiveness.
However, the number of clear-cut success stories for large, centralized comparative clinical trials has been modest, especially considering the vast number of treatment and resource allocation decisions where evidence is needed. Such trials are also costly to undertake, and necessarily only include a limited population.
By contrast, use of observational studies to provide evidence of comparative effectiveness has benefited from the growing availability of data from a wide range of sources, such as electronic health records, medical chart reviews, administrative data, and surveys. Such data can often be gathered more quickly and at a lower cost than in clinical trials; in addition, the use of more widely accessible, real-world populations can make the results more generalizable. Finally, new approaches to data analysis, such as the use of machine learning, may help overcome historical limitations of CER, wherein a well-designed clinical study may find no benefit on average while a specific subset of patients might still gain from the treatment.
For these and other reasons, in some situations observational data may be a better fit for decision makers as part of efforts to contain costs and maximize value for money spent. An analysis of recent literature in PubMed indicates that interest in real-world studies continues to rise while the number of CER studies has leveled off (see figure), which parallels a similar plateau seen for CER clinical trials (data not shown).
An equilibrium can likely be found that balances both approaches. Nevertheless, as the availability and interconnectedness of real-world data increase, comparisons of clinical and cost effectiveness using real-world data are likely to become the standard, with growing influence on the commercial success of new health technologies. ■
Hover over the lines in the chart below to show exact values for each year
Source: Analysis Group research. The NCBI PubMed database was searched using the terms “real world” and “comparative effectiveness” in title and abstract fields on 9/16/2016.
Adapted from “Perspectives on Decision Making in a World of Comparative Effectiveness Research,” by Dave Nellesen, Howard G. Birnbaum, and Paul E. Greenberg, in Decision Making in a World of Comparative Effectiveness Research, edited by Howard G. Birnbaum and Paul E. Greenberg, Springer Nature Singapore Pte Ltd., 2017.