In the feature, we peek behind the paper with lead study author Prachi Arora (Butler University; IN, USA) to better understand how real-world and comparative effectiveness evidence are associated with drug prescribing practices, with a focus on Type 2 diabetes therapies.
Please can you introduce yourselves and your institution(s)?
I am a health outcomes researcher and my current work focuses on assessing the impact of comparative effectiveness research (CER) on clinical decision making in diabetes and cardiovascular markets. I work as an Assistant Professor in the College of Pharmacy and Health Sciences at Butler University (IN, USA). My research and teaching interests also extend to the areas of health economics, pharmacoeconomics and health policies.
What prompted you to conduct this research?
It is of utmost importance, now more than ever, to curb the rising healthcare costs in the USA and find ways to ascertain the value of newer and costlier drug therapies. There is a growing emphasis on generating CER for drug therapies, however limited knowledge exists regarding the extent to which this evidence is incorporated into clinical decision making. This research was conducted with the purpose of evaluating the associations between CER evidence and trends in drug prescribing, to assess the bench-to-behavior translation of evidence within a therapeutic category.
How may real-world evidence (RWE) help researchers evaluate the value of new drug therapies?
RWE or CER can act as a strong tool to help researchers compare the benefits/harms, and quantify the value, of different drug therapies. The data can be generated using various study designs, namely meta-analyses and systematic reviews, randomized controlled trials and observational studies.
Generating RWE early on in the drug approval or drug marketing phase could help faster, smoother and more efficient integration of evidence into prescribing and coverage trends.”
The main purpose of RWE or CER is to examine the clinical and economic outcomes of drug therapies in real-world populations. CER provides direct comparative evidence to choose safer and more effective therapies, and in turn eliminate the alternatives that provide little or no added benefit.
What are some of the barriers towards integration of RWE into clinical practice and the decision-making process for drug prescribing and coverage trends?
One of the barriers towards integration of RWE into clinical practice is the absence of mandates around designing head-to-head superiority trials for new drug therapies. Lack of such regulations has led to the proliferation of unnecessary trials and studies, designed with the intent of proving simply non-inferiority of a new drug, rather than assessing its comparative benefit over an existing therapy. This in turn has made evidence translation more challenging and less efficient.
Another barrier is the timing of the availability of RWE for new drugs. Generating RWE early on in the drug approval or drug marketing phase could help faster, smoother and more efficient integration of evidence into prescribing and coverage trends.
How may we work towards faster and more improved translation of CER data into clinical practice and decision making?
We need to think in the direction of streamlining the generation of CER evidence and consolidating the evidence by developing more online tools, such as the CER Collaborative tool. It would be helpful if the consolidated reports or summaries assessing the value of new drug therapies are generated by unbiased entities and disseminated on a public forum to reduce nationwide inconsistencies in clinical practice.
…a meta-analysis is only as good and as robust as the randomized controlled trials included in it.”
Also, mandating the generation of comparative efficacy data at the time of a new drug approval might help quantify the quality of new therapies early on in the decision-making process. Additionally, regulations mandating the generation of superiority trials for new drugs using active comparators, prescribed widely in the real world, might be another step in the right direction.
What are some of the benefits of comparative effectiveness research (CER) study designs — such as meta-analyses and systematic reviews — compared with randomized controlled trial studies?
Meta-analyses and systematic reviews, if well conducted, following robust standards, have benefits over randomized controlled trials. Meta-analyses and systematic reviews can provide a systematic way to synthesize evidence, have a higher power and provide more precise treatment effects with lesser uncertainties compared with randomized trials. They also have the ability to combine data from smaller, inadequate trials and address research questions pertaining to specific population subgroups.
However, there have been recent arguments around decision makers refraining from adopting meta-analyses and systematic reviews in practice due to methodological flaws and heterogeneity issues presented by the study design. After all, a meta-analysis is only as good and as robust as the randomized controlled trials included in it.
What do you hope some of the practical applications of your research may be — how do you hope policy and decision makers will respond?
With this research I hope to emphasize the importance of superiority trials and the timing of their availability for Type 2 diabetes mellitus markets. Along with generating CER, it is important for organizations like the Agency for Healthcare Research and Quality (MD, USA), the US FDA and other policy makers to continue to devise strategies to make the translation of evidence more efficient.
With the increasing costs and diminishing quality of healthcare in the USA, I think it is impending that healthcare decision makers rely heavily on CER data and/or RWE to ascertain the value of healthcare.”
Further, this study provides a comprehensive literature evaluation of clinical data presented as CER evidence for drugs in the GLP1 agonist and DPP4 inhibitor therapeutic categories. With newer drugs being approved in these categories, this study hopes to provide a framework for further evidence evaluation, consolidation and dissemination. Additionally, even though this study compares two specific markets of Type 2 diabetes mellitus, the results can still be extrapolated to other markets, however any generalizations when doing so should be made with caution.
How do you see the integration and translation of CER data and/or RWE into clinical practice evolving in the future?
With the increasing costs and diminishing quality of healthcare in the USA, I think it is impending that healthcare decision makers rely heavily on CER data and/or RWE to ascertain the value of healthcare. We might soon need to follow the footsteps of some other organizations, such as NICE in the UK, and take stringent measures to develop a more concise and formal structure to identify, consolidate and summarize robust CER evidence, thus streamlining the bench-to-behavior translation of evidence into clinical practice.