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ICPE research spotlight: Part III: Standard-setting research to advance the use of RWE



This year at ICPE All Access, Aetion’s scientific research is represented across 15 posters and presentations spanning novel applications of real-world evidence (RWE), regulatory uses of real-world data (RWD), and innovative methods for epidemiologic research. In our “ICPE research spotlight” series, we share what you need to know about each, including important takeaways, a breakdown of the study design, and how the work shines new light on the ways RWE can inform decision-making.

While industry awaits guidance from the U.S. Food and Drug Administration (FDA) on RWE, stakeholders from across health care are collaborating on standard-setting initiatives to advance and inform the use of RWE by regulators and other decision-making bodies. 

In the following four posters and presentations, all prepared by Aetion scientists for ICPE All Access, we share research projects aimed at setting standards for RWE generation and use.

Read on to learn more about how external control arms generated from RWD can support regulatory submissions, how visualizations can facilitate transparency in RWE studies, how following principled database epidemiology can support credible RWE research, and how a decision guide for data feasibility assessments can enhance an RWE generation framework. 

For the full list of Aetion’s ICPE All Access research, see here

Review of oncology real-world comparator arm submissions in support of effectiveness claims in 2019 FDA original approvals reveals label-grade real-world study best practices
Ilker Oztop, Ph.D.; Wesley Eddings, Ph.D.; Amanda Patrick, M.S.; Pattra Mattox, S.M., C.M.P.P.; Christina Purpura, M.P.H.; Nicolle Gatto, Ph.D.

As interest in using RWD to generate external control arms increases, and these external controls are used to support regulatory submissions for new drugs and biologics, there is much to learn from past examples of RWE used in FDA approvals. While industry awaits the FDA’s formal RWE guidance, publicly available FDA statistical review documents help shed light on when and why an external control arm submission met the regulatory evidence requirements and was deemed fit to support a regulatory decision—or when it was not. 

This research shares a literature review of 2019 oncology new drug applications and biologics license applications that included an external control arm generated from RWD to support effectiveness or safety. Learn more about the FDA’s methodological critiques, learnings from an instance in which an external control arm was not deemed regulatory-grade, and best practices to help generate high quality RWD external controls. 

View the presentation here

Metabolic surgery and cardiovascular benefits: The consequence of information bias in an EHR-based real-world evidence study
William Murk, Ph.D., M.P.H.; Jeremy Rassen, Sc.D.; Sebastian Schneeweiss, M.D., Sc.D.

A recent study in the Journal of the American Medical Association by Aminian et al. reported that metabolic surgery is associated with a 39 percent reduction in risk of major adverse cardiovascular events (MACE) compared with non-surgery among obese patients with diabetes. 

Drs. Murk, Rassen, and Schneeweiss hypothesized that the results of that study were spurious due to differential information bias—people preparing for surgery receive a pre-diagnostic workup that could identify potential complications, which those who don’t undergo surgery wouldn’t receive. In this research, they replicated the study and modified its design so that the comparison groups had similar levels of baseline information. In the replicated study, they compared the metabolic surgery group to a control group consisting of patients undergoing an unrelated surgery, and used high-dimensional propensity scoring to match patients. The goal of the study was to demonstrate that metabolic surgery itself is not protective against MACE.

This work shows the importance of following principled database epidemiology when working with nonrandomized health care data, including selecting the appropriate control group. 

View the presentation here

Developing a Structured Process to Identify a Fit-for-purpose Data (SPIFD) framework
Ulka B Campbell, M.P.H., Ph.D.; Robert F Reynolds, Sc.D.; Emily Rubinstein, M.P.H.; Nicolle Gatto, Ph.D.

The SPACE framework, which stands for A Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real-world Evidence, established a process for designing valid and transparent real-world studies. 

This study aimed to build on the SPACE framework to provide researchers with a decision guide for conducting feasibility assessments to identify regulatory grade, fit-for-purpose data. Using the FDA’s definitions of regulatory-grade and fit-for-purpose RWD, researchers developed SPIFD to operationalize the steps needed to conduct data feasibility assessments. 

This structured process helps to ensure the systematic identification of candidate data sources that align with the criteria needed to address a research question. It offers an easy to digest graphical representation of the data to help communicate the rationale behind the selection of the data source. 

View the poster here

Visual space: Using visualization in pharmacoepidemiology to design and disseminate valid real-world evidence in a structured framework
Jeremy Rassen, Sc.D.; Shirley Wang, Ph.D.; Nicolle Gatto, Ph.D.; Alan Brookhart, Ph.D.; William Murk, Ph.D., M.P.H.

In order to generate credible, trustworthy RWE, researchers must ensure transparency throughout all stages of the study. Using visualizations can help reduce the risk of implementing poor study designs or misinterpreting the results, but there is no specific guidance around how visualization can support transparency. 

This symposium shares takeaways from an industry task force focused on setting standards around transparency, including strategies for visualizing study design. 

Learn more here, and tune into the live Q&A session on Thursday, December 10, 2020, at 10:00 a.m. EST. 

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