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McGrath LJ, Nielson C, Saul B, Breskin A, Yu Y, Nicolaisen SK, Kilpatrick K, Ghanima W, Christiansen CF, Bahmanyar S, Linder M, Eisen M, Wasser J, Altomare I, Kuter D, Sørensen HT, Kelsh M, Brookhart MA. Lessons Learned Using Real-World Data to Emulate Randomized Trials: A Case Study of Treatment Effectiveness for Newly Diagnosed Immune Thrombocytopenia. Clin Pharmacol Ther 2021; 110:1570-1578. [PMID: 34416023 DOI: 10.1002/cpt.2399] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/17/2021] [Indexed: 11/09/2022]
Abstract
Regulatory agencies are increasingly considering real-world evidence (RWE) to support label expansions of approved medicines. We conducted a comparative effectiveness study to emulate a proposed randomized trial of romiplostim vs. standard-of-care (SOC) therapy among patients with recently diagnosed (≤12 months) immune thrombocytopenia (ITP), that could support expansion of the romiplostim label. We discuss challenges that we encountered and solutions that were developed to address those challenges. Study size was a primary concern, particularly for romiplostim initiators, given the rarity of ITP and the stringent trial eligibility criteria. For this reason, we leveraged multiple data sources (Nordic Country Patient Registry for Romiplostim; chart review study of romiplostim initiators in Europe; Flatiron Health EMR linked with MarketScan claims). Additionally, unlike the strictly controlled clinical trial setting, platelet counts were not measured at regular intervals in the observational data sources, and therefore the end point of durable platelet response often used in trials could not be reliably measured. Instead, the median platelet count was chosen as the primary end point. Ultimately, while we observed a slightly higher median platelet count in the romiplostim group vs. SOC, precision was limited because of small study size (median difference was 11 × 109 /L (95% CI: -59, 81)). We underscore the importance of conducting comprehensive feasibility assessments to identify fit-for-purpose data sources with sufficient sample size, data elements, and follow-up. Beyond technical challenges, we also discuss approaches to increase the credibility of RWE, including systematic incorporation of clinical expertise into study design decisions, and separation between decision makers and the data.
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Affiliation(s)
| | - Carrie Nielson
- Center for Observational Research, Amgen, Thousand Oaks, California, USA
| | | | | | - Ying Yu
- NoviSci, Inc, Durham, North Carolina, USA
| | - Sia K Nicolaisen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Karynsa Kilpatrick
- Center for Observational Research, Amgen, Thousand Oaks, California, USA
| | - Waleed Ghanima
- Department of Hematology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Shahram Bahmanyar
- Clinical Epidemiology Division & Center for Pharmacoepidemiology, Karolinska Institutet, Stockholm, Sweden.,Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - Marie Linder
- Clinical Epidemiology Division & Center for Pharmacoepidemiology, Karolinska Institutet, Stockholm, Sweden
| | - Melissa Eisen
- Center for Observational Research, Amgen, Thousand Oaks, California, USA
| | | | | | - David Kuter
- Department of Hematology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Henrik T Sørensen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Michael Kelsh
- Center for Observational Research, Amgen, Thousand Oaks, California, USA
| | - M Alan Brookhart
- NoviSci, Inc, Durham, North Carolina, USA.,Department of Population Health Sciences, Duke University, Durham, North Carolina, USA
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Hak DJ, Mackowiak JI, Irwin DE, Aldridge ML, Mack CD. Real-World Evidence: A Review of Real-World Data Sources Used in Orthopaedic Research. J Orthop Trauma 2021; 35:S6-S12. [PMID: 33587540 DOI: 10.1097/bot.0000000000002038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/09/2020] [Indexed: 02/02/2023]
Abstract
SUMMARY Real-world data (RWD) play an increasingly important role in orthopaedics as demonstrated by the rapidly growing number of publications using registry, administrative, and other databases. Each type of RWD source has its strengths and weaknesses, as does each specific database. Linkages between real-world data sets provide even greater utility and value for research than single data sources. The unique qualities of an RWD data source and all data linkages should be considered before use. Close attention to data quality and use of appropriate analysis methods can help alleviate concerns about validity of orthopaedic studies using RWD. This article describes the main types of RWD used in orthopaedics and provides brief descriptions and a sample listing of publications from selected, key data sources.
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Affiliation(s)
- David J Hak
- Hughston Orthopaedic Trauma Surgeons, Central Florida Regional Hospital, Sanford, FL
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Abstract
SUMMARY The insights that real-world data (RWD) can provide, beyond what can be learned within the traditional clinical trial setting, have gained enormous traction in recent years. RWD, which are increasingly available and accessible, can further our understanding of disease, disease progression, and safety and effectiveness of treatments with the speed and accuracy required by the health care environment and patients today. Over the decades since RWD were first recognized, innovation has evolved to take real-world research beyond finding ways to identify, store, and analyze large volumes of data. The research community has developed strong methods to address challenges of using RWD and as a result has increased the acceptance of RWD in research, practice, and policy. Historic concerns about RWD relate to data quality, privacy, and transparency; however, new tools, methods, and approaches mitigate these challenges and expand the utility of RWD to new applications. Specific guidelines for RWD use have been developed and published by numerous groups, including regulatory authorities. These and other efforts have shown that the more RWD are used and understood and the more the tools for handling it are refined, the more useful it will be.
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Affiliation(s)
- Robert Zura
- Department of Orthopaedics, Louisiana State University Health Sciences Center, New Orleans, LA
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Demonstrating that Real World Evidence Is Fit-For-Purpose to Support Labeling: Parallels to Patient Reported Outcomes in the Pursuit of Labeling Claims. Ther Innov Regul Sci 2021; 55:561-567. [PMID: 33507517 DOI: 10.1007/s43441-020-00252-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 12/17/2020] [Indexed: 10/22/2022]
Abstract
INTRODUCTION In December 2021, U.S. Food & Drug Administration (FDA) will issue guidance on the use of real-world evidence (RWE) to support new indications or expanded product labeling. While difficult to foresee what FDA will require, learnings can be gleaned from previous paradigm shifts at FDA, such as for patient reported outcomes (PROs) in 2006-2009. METHODS We contrast published requirements for justifying PROs as fit-for-purpose for a specific labeling claim with a potential approach to justify RWE as fit-for-purpose to support expanded labeling or a new indication. RESULTS PRO labeling claims require a PRO Evidence Dossier that includes: specific wording of claim, clinical trial hypothesis structure and endpoint model, and justification that the PRO is relevant and meaningful to patients in the target population (content validity) with adequate psychometric properties. FDA's 2018 RWE Framework outlined critical considerations for using RWE to support regulatory decisions, including data quality, relevancy, provenance, and transparency. Strong parallels exist between the evidence required to justify that PROs are fit-for-purpose to support specific labeling claims and evidence to justify RWE as fit-for-purpose for specific research questions and labeling. Early discussion with FDA is encouraged. CONCLUSION Drawing on parallels with use of PROs in labeling, RWE for regulatory purposes should be evaluated within the context of specific labeling or indication, specific study design and analysis plans, and the data attributes of data source. Sponsors seeking a new indication or labeling expansion based on RWE should justify that a specific data source and specific study design are fit-for-purpose.
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