Evaluating the Use of Nonrandomized Real‐World Data Analyses for Regulatory Decision Making

2019 ◽  
Vol 105 (4) ◽  
pp. 867-877 ◽  
Author(s):  
Jessica M. Franklin ◽  
Robert J. Glynn ◽  
David Martin ◽  
Sebastian Schneeweiss
Keyword(s):  
Decision Making ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Data Analyses

scholarly journals Health Canada & Canadian Society for Pharmaceutical Sciences - Use of Real World Data/Evidence to Inform Regulatory Decision Making

2020 ◽  
Vol 23 ◽  
pp. 1s-47s
Author(s):  
Real World Data Workshop Group CSPS/Health Canada
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Clinical Trials ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Regulatory Agencies ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Health Canada

Real world data (RWD) and real world evidence (RWE) are playing increasing roles in health-care decisions. Real world data are routinely employed to support reimbursement and coverage decisions for drugs and devices. More recently, clinical trials incorporating pragmatic designs and observational studies are considered to supplement traditional clinical trials (e.g., randomized clinical trials). Regulatory agencies and large co-operative groups including academia and industry are exploring whether leveraging big databases such as electronic medical records and claims databases can be used to garner clinical insights extending beyond those gained from randomized controlled studies. Whether RWE can ultimately replace or improve traditional clinical trials is the big question. The workshop held on December 3, 2019 at Health Canada included presenters from regulatory agencies, industry and academia. Health Canada, US FDA and European Medicine Agency presented current thinking, draft frameworks and guidance available in the public domain. While the three agencies might be at different stages of utilizing RWE for regulatory decision making, the consensus is not whether RWE would be used but when and how it can be incorporated into regulatory decision making while maintaining a high evidentiary bar. The complexity of data sourcing, curating databases, aligning on common data models, illustrated by high-profile work conducted as part of Sentinel, DSEN, OHDSI and Duke-Margolis initiatives, was presented and discussed during the workshop, creating great learning opportunities for the attendees. The design and analysis of RWE studies were compared and contrasted to those of RCTs. While there are gaps, they are closing quickly as novel analytical methods are employed and innovative ways of curating data, including natural language processing and artificial intelligence, are explored.   This proceeding contains summaries of information presented by the speakers, including current highlights about the use of RWE in regulatory decision making. In the world where the uptake of “big data” in everyday life is happening at unprecedented speed, we can expect RWE to be a fast-moving area and with the potential for big impact in health-care decision making in the years to come.

scholarly journals Applying real-world data to support regulatory decision — making in the United States

2021 ◽  
Vol 20 (1) ◽  
pp. 64-69
Author(s):  
M. A. Borzova ◽  
А. S. Kolbin
Keyword(s):  
United States ◽  
Decision Making ◽  
Real World ◽  
The United States ◽  
Legal Basis ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Eurasian Economic Union ◽  
Economic Union

The article describes the legal basis for the application of real-world data to support regulatory decision-making in the United States, as well as the possibility of implementing the relevant approaches in the legislation of the Eurasian Economic Union.

Real-World Data Analytics Fit for Regulatory Decision-Making

2018 ◽  
Vol 44 (2-3) ◽  
pp. 197-217 ◽  
Author(s):  
Sebastian Schneeweiss ◽  
Robert J. Glynn
Keyword(s):  
Decision Making ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Routine Care ◽  
Added Value ◽  
Real World Data ◽  
Healthcare Database ◽  
World Data ◽  
Medical Products ◽  
Regulatory Decision

Healthcare database analyses (claims, electronic health records) have been identified by various regulatory initiatives, including the 21st Century Cures Act and Prescription Drug User Fee Act (“PDUFA”), as useful supplements to randomized clinical trials to generate evidence on the effectiveness, harm, and value of medical products in routine care. Specific applications include accelerated drug approval pathways and secondary indications for approved medical products. Such real-world data (“RWD”) analyses reflect how medical products impact health outside a highly controlled research environment. A constant stream of data from the routine operation of modern healthcare systems that can be analyzed in rapid cycles enables incremental evidence development for regulatory decision-making.Key evidentiary needs by regulators include 1) monitoring of medication performance in routine care, including the effectiveness, safety and value; 2) identifying new patient strata in which a drug may have added value or unacceptable harms; and 3) monitoring targeted utilization. Four broad requirements have been proposed to enable successful regulatory decision-making based on healthcare database analyses (collectively, “MVET”): Meaningful evidence that provides relevant and context-informed evidence sufficient for interpretation, drawing conclusions, and making decisions; valid evidence that meets scientific and technical quality standards to allow causal interpretations; expedited evidence that provides incremental evidence that is synchronized with the decision-making process; and transparent evidence that is audible, reproducible, robust, and ultimately trusted by decision-makers.Evidence generation systems that satisfy MVET requirements to a high degree will contribute to effective regulatory decision-making. Rapid-cycle analytics of healthcare databases is maturing at a time when regulatory overhaul increasingly demands such evidence. Governance, regulations, and data quality are catching up as the utility of this resource is demonstrated in multiple contexts.

scholarly journals Real‐World Data for Regulatory Decision Making: Challenges and Possible Solutions for Europe

2019 ◽  
Vol 106 (1) ◽  
pp. 36-39 ◽  
Author(s):  
Alison Cave ◽  
Xavier Kurz ◽  
Peter Arlett
Keyword(s):  
Decision Making ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision

scholarly journals FDA Oncology Center of Excellence Review of Real World Data Submissions Supporting Drug Development in Hematologic Malignancies

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 5037-5037
Author(s):  
Donna R. Rivera ◽  
Jennifer J Lee ◽  
Melanie Royce ◽  
R. Angelo de Claro ◽  
Nicole J. Gormley ◽  
...  
Keyword(s):  
Decision Making ◽  
Overall Survival ◽  
Real World ◽  
Response Rate ◽  
Hematologic Malignancies ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Real World Evidence ◽  
Regulatory Submissions

Abstract Background Aligning with 21st Century Cures legislation, FDA is exploring methodologies to advance appropriate uses of Real World Data (RWD) to generate Real World Evidence (RWE). RWD to support regulatory decision making has markedly increased in oncology. This review specifically focused on the analysis of RWD containing submissions for medical products in development for the treatment of hematological malignancies and associated treatment related conditions (e.g., Cytokine Release Syndrome (CRS), Graft Versus Host Disease (GVHD). Methods A systematic search was conducted using internal FDA databases to identify RWD submissions from 2010 to 2020. Search terms included: real world evidence, real world data, electronic health record, cancer registry, administrative claims, external control arm, observational cohort, historical control arm, real world Overall Survival (rwOS) , real world Response Rate (rwRR), real world Overall Response Rate rwORR and real world Complete Response (rwCR). Regulatory submissions specific to malignant hematology and associated treatment related conditions were reviewed, and pre-defined common data elements were extracted and validated by independent dual review. Descriptive statistics were calculated. Results A total of 142 regulatory submissions included RWD from 2011-2020. A subset of 94 RWD submissions met the criteria for further evaluation, of which 20 (21%) submissions corresponding with 14 molecular entities were for hematologic malignancies or treatment related conditions (e.g., CRS, GVHD). RWD submissions increased substantially over time, with 14 (70%) of submissions received between 2019-2020. Specific evaluation for pediatric indications was referenced in 15% of submissions. The most commonly referenced RWD source was EHR data (55%), followed by use of multiple sources (20%), and registry data (15%). Approximately 90% of the submissions aimed to support treatment effectiveness. Primary RWD study objectives included supporting approval of a new molecular entity (NME) (40%), expanding an approved indication (25%), conversion from accelerated to regular approval (15%), and providing data to inform postmarketing safety evaluation (20%). Among RWD submissions, response endpoints (e.g., rwORR, rwCR, rwPR, Partial Response) and overall survival (e.g., rwOS) were most frequently selected as primary outcomes for 50% and 20% of proposals respectively; however, these outcomes were included as any endpoint in 65% and 75% of submissions. Conclusion This review demonstrates increasing use of various RWD sources to support evidence generation for drug development in hematologic malignancies and associated treatment related conditions with the primary objective of supporting demonstration of effectiveness using rwOS or real world response measures as primary endpoints. Given the increased inclusion of RWD in regulatory submissions, further methodological development is needed, including in the selection and validation of rwEndpoints. Appropriate study design must be aligned with a clear regulatory objective to ensure that RWD can be adequately evaluated. Additionally, the development of standardized metrics for data characterization and transparency in reporting of RWD are foundational steps to the evaluation of fit for purpose RWD to support regulatory decision making. Disclosures No relevant conflicts of interest to declare.

FDA Oncology Center of Excellence landscape analysis of real-world data submissions for oncology drugs.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18787-e18787
Author(s):  
Donna Rivera ◽  
Jennifer J. Lee ◽  
Melanie E Royce ◽  
Paul Gustav Kluetz
Keyword(s):  
Decision Making ◽  
Real World ◽  
Landscape Analysis ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Real World Evidence ◽  
Regulatory Submissions ◽  
Oncology Center ◽  
Oncology Drug Development

e18787 Background: Aligning with 21st Century Cures legislation, the FDA is exploring trial design modernization and methodology to advance appropriate uses of Real World Data (RWD) to generate Real World Evidence (RWE). The Oncology Center of Excellence RWE Program was established in 2020 to advance RWE efforts specific to oncology drug development. Inclusion of RWD to support regulatory decision making has increased in oncology, and a landscape analysis was conducted to characterize the RWD included in submissions. Methods: A systematic search was conducted using internal FDA databases to identify RWD submissions from 2010 to 2020. Search terms included: real world evidence, real world data, electronic health record, cancer registry, administrative claims, external control arm, observational cohort, historical control arm, rwOS, rwRR, rwCR, and rwORR. Relevant regulatory submissions were reviewed, and pre-defined common data elements were extracted. A team of FDA reviewers assessed agreement through subset validation (20%). Descriptive statistics were calculated. Results: A total of 142 regulatory submissions included RWD from 2011 to 2020. A subset of 94 submissions met the criteria for evaluation, consisting of 78 unique studies evaluating 56 molecular entities. RWD submissions increased substantially over time, with 28 submissions in 2020. Nearly half of the RWD submissions were for solid tumor indications (68%), with lung cancer being the most predominant site. More than one third of the RWD submissions (37%) were for rare indications. The most common primary RWD study objective was effectiveness (62%) and the most commonly referenced RWD source was EHR/clinical data (54%). The most frequently used primary RWD endpoints were survival (rwOS, 35%) and response (rwORR/PR/BTR, 31%) outcomes (Table). Conclusions: Our review demonstrates a dramatic increase in RWD submissions to support FDA oncology drug development programs. Submissions included a variety of study objectives, data sources, and endpoints. While this landscape analysis provides a picture of potential regulatory objectives, the adequacy of each proposal to support regulatory decision making was not evaluated. Establishing a set of clear regulatory objectives can help advance the development of metrics for robust data characterization and outcome validation to ensure that RWD can be appropriately evaluated and provide the rigor necessary to be considered adequate RWE.[Table: see text]

Role of real world data and real world evidence in regulatory decision making

2021 ◽  
Vol 14 (2) ◽  
pp. 1177-1182
Author(s):  
B. C. Bharath ◽  
V. Balamuralidhara ◽  
M. P. Gowrav
Keyword(s):  
Decision Making ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Real World Evidence

Real‐world data in Saudi Arabia: Current situation and challenges for regulatory decision‐making

2020 ◽  
Vol 29 (10) ◽  
pp. 1303-1306
Author(s):  
Fatemah A. Alnofal ◽  
Adel A. Alrwisan ◽  
Thamir M. Alshammari
Keyword(s):  
Decision Making ◽  
Saudi Arabia ◽  
Real World ◽  
Current Situation ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision
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