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RWR Insights | USA – Lessons Learned from FDA Reviews of External Control Arms

RWR CONTEXT

In February 2023, the FDA published a draft guidance document on considerations for the design and conduct of externally controlled trials and external control arms.  The aim being to help address and prevent the common mistakes and methodological design flaws that limit the acceptability of external controls for regulatory decision making.

Here we explore some recent examples of external control arms reviewed by the FDA, highlight the limitations identified, and provide recommendations (Lessons Learned) for optimising the acceptability of external controls for regulatory decision making.

FEBRUARY 2023 – On the 1 February 2023 the FDA published draft guidance on ‘Considerations for the Design and Conduct of Externally Controlled Trials for Drug and Biological Products’. We have provided a summary of this new draft FDA guidance earlier in the report…Read More.

Here we explore some recent examples of external control arms reviewed by the FDA, highlight the limitations identified, and provide recommendations (Lessons Learned) for optimising the acceptability of external controls for regulatory decision making.

Based on the 3 case studies below, the FDA identified the following common limitations to the acceptability of external controls for regulatory decision making:

      • Post-hoc analysis (lack of prior FDA review of the protocol and SAP)
      • Selection Criteria Issues (selection bias, misclassification, and confounding)
      • Index Date Issues (immortal time bias)
      • Comparability Issues (lack of comparability, missing data, confounding bias)
      • Limited Sample Size (Lack of statistical power due to insufficient real world cohort size)
      • Real world data not fit for purpose

First, let’s set the foundation and have a quick look at the purpose of  a control group in a clinical trial and the trends and uses of external control arms.

What is the Purpose of a Control Group?

      • Control groups have one major purpose: to allow discrimination of patient outcomes (for example, changes in symptoms, signs, or other morbidity) caused by the test treatment from outcomes caused by other factors, such as the natural progression of the disease, observer or patient expectations, or other treatment (Section 1.2 of ICH E10) [2]. 
      • The control group experience tells us what would have happened to patients if they had not received the test treatment or if they had received a different treatment known to be effective (Section 1.2 of ICH E10) [2]. 
      • In most situations, a concurrent control group is needed because it is not possible to predict outcome with adequate accuracy or certainty (Section 1.2 of ICH E10) [2]. 
      • A concurrent control group is one chosen from the same population as the test group and treated in a defined way as part of the same trial that studies the test treatment, and over the same period of time (Section 1.2 of ICH E10) [2]. 
      • The test and control groups should be similar with regard to all baseline and on-treatment variables that could influence outcome, except for the study treatment (Section 1.2 of ICH E10) [2]. 
      • Failure to achieve this similarity can introduce a bias into the study (Section 1.2 of ICH E10) [2]. 
      • Bias here means the systematic tendency of any aspects of the design, conduct, analysis, and interpretation of the results of clinical trials to make the estimate of a treatment effect deviate from its true value (Section 1.2 of ICH E10) [2]. 
      • Randomization and blinding are the two techniques usually used to minimize the chance of such bias and to ensure that the test treatment and control groups are similar at the start of the study and are treated similarly in the course of the study. Whether a trial design includes these features is a critical determinant of its quality and persuasiveness (Section 1.2 of ICH E10) [2]. 

External Control Arms – Trends and Uses

      • Accelerated approval programs for high morbidity and high unmet need diseases have driven the use of single-arm studies, studies that do not include placebo or active comparator arms (ie, no concurrent control), for drug development (Jaksa et al., 2022) [3]. 
      • External control arms (ECAs) generated from real world data (RWD) are emerging to contextualize single-arm trial data by exploring what would happen if single-arm trial patients did not receive the study drug (Jaksa et al., 2022) [3].
      • Single-arm studies speed up patient access to innovative treatments because they often require fewer patients than randomized controlled trials (RCTs) and use intermediate or surrogate endpoints (e.g., objective response rate [ORR]) (Jaksa et al., 2022) [3].
      • Oncology drug development has been increasingly relying on single-arm studies: from 1992 to 2017, 67% of the Food and Drug Administration’s (FDA) accelerated approvals were based on single-arm trials (Jaksa et al., 2022) [3].
      • Similar trends have been observed in health technology assessment (HTA) submissions. From 2000 to 2016, 22 submissions to the National Institute for Health and Care Excellence (NICE) in the UK were based on non-randomized data, and oncology drugs accounted for more than half of these submissions (Jaksa et al., 2022) [3].

Case Study #1 – Rozlytrek (Entrectinib) for the Treatment of ROS1-Positive, Advanced Non-Small Cell Lung Cancer (NSCLC)

 

FDA (NDA 212725) = Priority Review. Breakthrough Therapy. Orphan Drug Designation. Accelerated Approval (15 August 2019) [4] [5].

This study report reviewed by the FDA contained a comparative analysis of time to treatment discontinuation (TTD), progression free survival (PFS), and overall survival (OS). It compared patients with ROS1-positive NSCLC receiving entrectinib in three single arm clinical trials (ALKA, STARTRK-1, STARTRK-2) and patients with ROS1-positive NSCLC receiving crizotinib in the real world captured by the Flatiron Health Analytic Database [6].

Is the Crizotinib RWE arm sufficient to establish the natural history of disease for ROS1-positive NSCLC?

FDA Review [7]:

      • The crizotinib arm is unlikely to be generalizable to the entire population of patients with ROS1-positive NSCLC
      • The generalizability of the RWE control arm was limited by the low rate of ROS1 testing in clinical practice and resultant sensitivity (estimated as 15%-30%) and the high proportion of community-treated patients in the selected data source.
      • Examination of baseline characteristics demonstrates that the crizotinib arm is not sufficiently comparable to the entrectinib clinical trial population.

FDA Review Conclusions [8]:

      • While the crizotinib population identified may be representative to patients who currently receive treatment for ROS1-positive NSCLC in the community setting, it is not generalizable to the entire ROS1-positive NSCLC population and it is not generalizable to patients enrolled in entrectinib clinical trials. 

Does the study methodology provided allow for a comparison of treatment outcomes between the entrectinib arm and crizotinib arm in this study? 

FDA Review [7]:

      • The study identified substantial differences in time to treatment discontinuation (TTD), progression-free survival (PFS), and overall survival (OS) between study arms, all favoring the entrectinib arm
      • Differentially implemented study eligibility criteria, resultant differences in baseline criteria, and limitations in statistical modeling due to low sample size make it difficult to determine what proportion of the observed differences in rates of clinical outcomes are due to imbalances in study populations at baseline (i.e. selection bias) versus differential treatment effects of the study drugs = This limits comparison of study arms. 
      • Additionally, despite a well-done attempt at defining treatment outcomes, there were limitations. TTD is complicated by treatment beyond disease progression, PFS is limited by missingness in radiographic imaging within electronic medical record data, and OS may be more subject to bias from baseline imbalances

FDA Review Conclusions [8]:

      • This study report is not adequate to allow a robust comparison of treatment outcomes between crizotinib and entrectinib study arms.

List of key external control arm limitations identified by the FDA [9]:

      • Post-hoc analysis (lack of prior FDA review of the protocol and statistical analysis plan)
      • Selection Bias
          • This is the greatest threat to study validity for the comparison of study arms. Substantial differences in baseline covariates were observed. While this is a generally well-done study report, it is unlikely these differences can be overcome with the provided analyses [10]
      • Missing Data Among Covariates and Missing Covariates
          • The Applicant did not try to replicate all the study eligibility criteria in this RWE protocol, likely because data to implement them are missing for many inclusion and exclusion criteria in the crizotinib RWE arm [10]. 
          • It would have been useful for the Applicant to evaluate all eligibility criteria to the extent possible, especially baseline laboratory data [10]. 
          • It is noteworthy that ECOG was missing in 55.1% of patients in the crizotinib arm [10]
      • Statistical Modelling…limited by sample size
      • Measure of Study Outcomes
          • This study report provides a generally acceptable definition of study outcomes given limitations of available data. It does have limitations. Time to Treatment Discontinuation (TTD) is complicated by treatment beyond disease progression, Progression Free Survival (PFS) is limited by lack of radiographic imaging in EMR data, and Overall Survival (OS) may be more subject to bias from baseline imbalances [10].
          • This study report is not adequate to allow a robust comparison of treatment outcomes between crizotinib and entrectinib study arms [8].

 

Impact on Rozlytrek (Entrectinib) Approval = Label limitations. The resulting FDA label of Rozlytrek excluded Time to Treatment Discontinuation (TTD), Progression Free Survival (PFS), and Overall Survival (OS) outcomes, and only referenced improvements in Overall Response Rate (ORR) [11]. 

Case Study #2 – Xpovio (selinexor)  for the Treatment of Adult Patients with Relapsed or Refractory Multiple Myeloma (RRMM)

FDA (NDA 212306)(Xpovio) = Regular Review. Orphan Designation. Accelerated Approval (3 July 2019) [12] [13]

In support of the NDA 212306 for selinexor, the Applicant submitted the results of a phase 2b, open label, single arm clinical trial (STORM) and an external control arm from analyses using retrospectively collected electronic health record (EHR) data (Study KS-50039 ) – below [12]:

Methodological Issues Identified by the FDA [14]: 

      • Selection Criteria Issues (selection bias, misclassification, and confounding)
          • Substantial differences in the inclusion and exclusion criteria for the STORM and the external control (Flatiron Health Analytical Database (FHAD) cohorts) are likely to result in selection bias, misclassification, and confounding. 
          • For example, the Applicant cited real-world overall survival (OS) of patients with penta-exposed, triple-class refractory MM as 3.5–3.7 months; however, patients with less than 4 months life expectancy were excluded from STORM. An exclusion criterion for minimal life expectancy was not implemented for the FHAD population. Differences in selection criteria between the study arms systematically ensure that the STORM cohort will have longer expected OS compared to FHAD cohort  
      • Index Date Issues (immortal time bias)
          • Systematic differences in how the index date was defined may have resulted in biased results (immortal time bias).
          • The definition of the index date has a direct effect on the length of the observed survival time intervals. 
          • The index date to start assessment of overall survival, for both the STORM trial and FHAD, was the date upon which a patient failed his or her last treatment. Using this index date, some FHAD patients could have exhausted all treatment options and could not be indexed at their next treatment (FDA note that 27/64 FHAD patients had no subsequent treatment and should have been excluded from the study). 
          • However, in STORM, all patients must survive until randomization (initiation of selinexor) by design. Thus, person-time between failure of the prior therapy and randomization is “immortal” by design in STORM. It is unknown how many months of immortal time this represents on average. 
      • Comparability Issues (lack of comparability, missing data, confounding bias)
          • oIn addition to difference in inclusion and exclusion criteria, additional factors result in a lack of comparability between the FHAD and STORM cohorts. 
          • RWD analysis compares patients in STORM, who are sufficiently healthy to enroll in a clinical trial, versus patients in FHAD who may or may not receive additional therapy. 
          • Patients who have failed their current treatment but do not receive another treatment are likely have a lower expectation for overall survival. 
          • oPatients who would likely have been more similar to the STORM cohort were explicitly excluded from the FHAD cohort.
          • FHAD had different prior treatment histories, and differential distributions of ECOG scores and missing data. 
          • Imbalances between treatment groups were not adequately accounted for in the design or analysis phases, which likely resulted in confounding bias, primarily favoring survival for the STORM cohort. 

FDA Review Conclusions [15]:

      • To enhance transparency and facilitate evaluation of validity, FDA requires submission of study protocols and statistical analysis plans (SAP) prior to study initiation. Pre-specification of study protocols and SAPs can preclude unplanned multiple testing and analyses, which may inflate Type I error probability and lead to spurious or un-reproducible findings. In support of NDA 212306 for selinexor, the Applicant submitted analyses using retrospectively collected electronic health record (EHR) data. However, neither the protocol or SAP for the selinexor RWD analysis was submitted to FDA prior to the conduct of the study. FDA was made aware of Study KS-50039 upon receiving the final study report on October 6, 2018 [15]. 
      • Given the methodological limitations discussed above, we conclude that the evidence generated from the RWD analysis is not adequate to provide context or comparison for the overall survival observed in the STORM patients. This conclusion is based on the lack of comparability between the STORM and FHAD treatment groups. Furthermore, FDA’s analysis finds that post-hoc strategies to create greater comparability across cohorts were inadequate and resulted in very limited sample size and unstable estimates [15].
      • Due to major methodological issues (including immortal time bias, selection bias, misclassification, confounding, and missing data), the FDA does not consider these results adequate to support regulatory decision making [15].

Impact on Xpovio (selinexor) Approval = The Oncology Drug Advisory Committee (ODAC) members voted in favor of delaying approval until results of the randomized phase 3 BOSTON trial are available [16].

Case Study #3 – Abecma (idecabtagene vicleucel/ide-cel) for the Treatment of Multiple Myeloma

FDA (BLA 125736)(Abecma) = Priority Review. Orphan Designation. Breakthrough Therapy Designation. Regular Approval (26 March 2021) [17] [18].

Idecabtagene vicleucel was approved for fourth-line relapse or refractory multiple myeloma by the FDA in 2021. The clinical evidence included a phase 2b, single-arm, multi-centre clinical trial (MM-001) and an external control arm (ECA) generated from real world data across multiple sources including clinical sites, registries, and a research database (Real world evidence study NDS-MM-003) . The goal of the external control arm was to provide an estimate of overall remission rate (ORR) in patients receiving at least 3 previous myeloma regimens [3] [19].

Study MM-001 – Enrolled 140 subjects and 127 were infused with conformal ide-cel [19]. 

Study NDS-MM-003 (Retrospective Observation Study using Real-World Data) – A global non-interventional retrospective study (NDS-MM-003) to compare the outcome of MM-001 study with a real-world cohort of relapsed and refractory myeloma patients treated with standard therapies. Patient level data from clinical sites, registries and research database was collated into a single data model using data cut off of 30 October 2019. Subjects in the eligible RRMM cohort received approximately 90 different treatment regimens predominantly as a combination of 3 or more drug regimens [20]. 

FDA Review Comments to the External Control Arm (Study NDS-MM-003) [20]:

      • The Agency communicated concerns about the real-world evidence (RWE) study (NDS-MM-003) which was being conducted to provide an indirect comparison of effectiveness of bb2121.
      • Issues with the RWE study include selection of a population which may not be comparable to subjects enrolled in Study MM-001 due to missing baseline patient characteristics, missing or absent data on efficacy assessments which may bias the outcomes and heterogeneity of real world data from different databases that will be collated for analysis. 
          • Selection criteria issues
          • Missing data
          • Real world data not fit for purpose
      • Given the methodological limitations discussed above, we conclude that the evidence generated from the RW analysis is not adequate to provide context or comparison for the outcome of MM-001 study.
          • Significant amount of missing data
          • Differences in follow up and response assessment of subjects from these different sources may impact the interpretability of the study results. 
          • Significant heterogeneity in the RWE population limits its utility as a control arm.
          • The follow up schedule for response assessment in RWE and clinical trial myeloma patients may be different. This can result in potential bias in the estimate of duration of response. 
          • Different response assessments for clinical trial vs RWE patients
          • The efficacy results from the RWE study population are uninterpretable as compared to efficacy evaluable population determined by the Agency (N=100) and based on FDA adjudicated efficacy results.
      • While it reiterates the challenges of an appropriate choice of a treatment in the control arm and supports the approach of considerations for a single arm study design in support of a primary study intended for marketing purposes, an alternative approach may be to consider a randomized controlled trial with investigator’s choice of treatment from pre-specified therapeutic options as the control arm.

FDA Review Conclusions for Abecma [20]:

      • Given the methodological limitations discussed above, we conclude that the evidence generated from the external control arm (Study NDS-MM-003) is not adequate to provide context or comparison for the outcome of MM-001 study. 

Conclusions and Recommendations

The FDA identified the following common limitations in the external control arms of the 3 case studies above:

      • Post-hoc analysis (lack of prior FDA review of the protocol and SAP)
      • Selection Criteria Issues (selection bias, misclassification, and confounding)
      • Index Date Issues (immortal time bias)
      • Comparability Issues (lack of comparability, missing data, confounding bias)
      • Limited Sample Size (Lack of statistical power due to insufficient real world cohort size)
      • Real world data not fit for purpose

Recommendations for optimising the acceptability of external controls for regulatory decision making, include:

      • Apply the guidance provided in the new draft FDA guidance on the design and conduct of externally controlled trials [1]
      • Design the external control to emulate the preferred randomised controlled trial – use a “target trial approach”
      • Use appropriate Real world data sources to optimise comparability, relevance and generalizability of the control group population
      • Seek early FDA review of the protocol and statistical analysis plan (a priori rather than post hoc)
      • Assess and limit bias using the appropriate methodological/ statistical designs
      • Use the START-RWE templates to demonstrate the what, how and why of the data sources, curation and analysis [21] [22]
      • List the limitations of the real world data/ real world evidence in the protocol

References

1. FDA Draft Guidance – Considerations for the Design and Conduct of Externally Controlled Trials for Drug and Biological Products (February 2023)

Link: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-design-and-conduct-externally-controlled-trials-drug-and-biological-products 

 

2. ICH E10 – Choice of Control Group in Clinical Trials (July 2000)

Link: https://database.ich.org/sites/default/files/E10_Guideline.pdf 

3. Ashley Jaksa, Anthony Louder, Christina Maksymiuk, Gerard T. Vondeling, Laura Martin, Nicolle Gatto, Eric Richards, Antoine Yver, Mats Rosenlund. A Comparison of Seven Oncology External Control Arm Case Studies: Critiques From Regulatory and Health Technology Assessment Agencies. Value in Health, 2022. ISSN 1098-3015, doi.org/10.1016/j.jval.2022.05.016. (25 June 2022)

Link: https://www.valueinhealthjournal.com/article/S1098-3015(22)02004-6/fulltext?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1098301522020046%3Fshowall%3Dtrue  

4. FDA approves entrectinib for NTRK solid tumors and ROS-1 NSCLC (August 2019) 

Link: https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-entrectinib-ntrk-solid-tumors-and-ros-1-nsclc  

5. Drugs@FDA: FDA-Approved Drugs – Rozlytrek (NDA 212725)

Link: https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm?event=overview.process&ApplNo=212725 

6. Section 3.1 – Study Overview (page 7) – CDER – Review of Study Report No WO40977: Comparative analysis of ROS1-positive locally advanced or metastatic non-small cell lung cancer between patients treated in entrectinib trials and crizotinib treated patients from real world data (11 July 2019)

Link: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212725Orig1s000,%20212726Orig1s000OtherR.pdf  

7. CDER – Review of Study Report No WO40977: Comparative analysis of ROS1-positive locally advanced or metastatic non-small cell lung cancer between patients treated in entrectinib trials and crizotinib treated patients from real world data (11 July 2019)

Link: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212725Orig1s000,%20212726Orig1s000OtherR.pdf  

8. Section 6 – Recommendations (page 28) – CDER – Review of Study Report No WO40977: Comparative analysis of ROS1-positive locally advanced or metastatic non-small cell lung cancer between patients treated in entrectinib trials and crizotinib treated patients from real world data (11 July 2019)

Link: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212725Orig1s000,%20212726Orig1s000OtherR.pdf   

9. Section 8 – Appendix (page 30) – CDER – Review of Study Report No WO40977: Comparative analysis of ROS1-positive locally advanced or metastatic non-small cell lung cancer between patients treated in entrectinib trials and crizotinib treated patients from real world data (11 July 2019)

Link: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212725Orig1s000,%20212726Orig1s000OtherR.pdf  

10. Section 4 – Discussion (page 27) – CDER – Review of Study Report No WO40977: Comparative analysis of ROS1-positive locally advanced or metastatic non-small cell lung cancer between patients treated in entrectinib trials and crizotinib treated patients from real world data (11 July 2019)

Link: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212725Orig1s000,%20212726Orig1s000OtherR.pdf  

11. Learnings from three FDA decisions on ECA submissions in oncology, Aetion (November 2019)

Link: https://aetion.com/evidence-hub/learnings-from-three-fda-decisions-on-eca-submissions-in-oncology/  

12. NDA 212306: Selinexor – Oncologic Drugs Advisory Committee Meeting – Introductory Comments – February 26, 2019

Link: https://www.fda.gov/media/121670/download 

13. Drugs@FDA: FDA-Approved Drugs – Xpovio (NDA 212306)

Link: https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm?event=overview.process&ApplNo=212306 

14. Section 7.2.6 (KS-50039 (Retrospective observational study using real-world data)) (page 84) – FDA – NDA/BLA Multi-disciplinary Review and Evaluation, NDA 212306, XPOVIO® (selinexor) (July 2019) 

Link: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212306Orig1s000MultidisciplineR.pdf 

15. 1.Section 7.2.6 (KS-50039 (Retrospective observational study using real-world data)) (page 84) – FDA – NDA/BLA Multi-disciplinary Review and Evaluation, NDA 212306, XPOVIO® (selinexor) (July 2019) 

Link: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212306Orig1s000MultidisciplineR.pdf 

16. Section 1.2 (Conclusions on the Substantial Evidence of Effectiveness ) (page 16) – FDA – NDA/BLA Multi-disciplinary Review and Evaluation, NDA 212306, XPOVIO® (selinexor) (July 2019) 

Link: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212306Orig1s000MultidisciplineR.pdf 

17. FDA – ABECMA (idecabtagene vicleucel) (21 April 2021)

Link: https://www.fda.gov/vaccines-blood-biologics/abecma-idecabtagene-vicleucel 

18. FDA – Summary Basis for Regulatory Action – Abecma (26 March 2021)

Link: https://www.fda.gov/media/147627/download 

19. FDA – BLA 125736 (Abecma) – Statistical  Review (27 July 2020)

Link: https://www.fda.gov/media/147781/download 

20. Section 9.2 (Aspect(s) of the Clinical Evaluation Not Previously Covered – Study NDS-MM-003 (Retrospective Observation Study using Real-World Data))(page 113) – FDA BLA Clinical Review Memorandum – STN 125736/0 (27 July 2020)

Link: https://www.fda.gov/media/147740/download 

21. Wang S V, Pinheiro S, Hua W, Arlett P, Uyama Y, Berlin J A et al. STaRT-RWE: structured template for planning and reporting on the implementation of real world evidence studies BMJ 2021; 372 :m4856 doi:10.1136/bmj.m4856

Link: https://www.bmj.com/content/372/bmj.m4856 

22. Harvard Dataverse – Structured Template and Reporting Tool for Real World Evidence (STaRT-RWE)

Link: https://dataverse.harvard.edu/dataverse/STaRT-RWE;jsessionid=952c11f48e3021a6ddcd8c9c9822 

RWR Insights | USA – Lessons Learned from FDA Reviews of External Control Arms2023-03-05T12:37:05+00:00

EU | EMA 2025 Vision for the Regulatory Use of RWE – Connecting the Pieces

RWR CONTEXT

In the past 12 months, the EMA has been busy…publishing strategies and frameworks, creating coordination centres, and requesting proposals from training vendors! The question is…how does this all fit together?

Here we provide a a high-level overview of what has happened and what role each of the activities plays in helping to realise the EMA’s 2025 vision for the use of RWE in regulatory decision making.

DECEMBER 2022 – The European Medicines Agency (EMA) has a very ambitious goal for the use of real-world evidence (RWE):

“…by 2025 the use of real-world evidence will have been enabled and the value will have been established across the spectrum of regulatory use cases”

[EMA – A vision for use of real-world evidence in EU medicines regulation (24 November 2021)] [ref 1]

In the past 12 months, the EMA has been busy…publishing strategies and frameworks, creating coordination centres, and requesting proposals from training vendors! The question is…how does this all fit together?  The following image provides a high-level overview of what has happened and what role each of the activities plays in helping to realise the EMA’s 2025 vision for the use of RWE in regulatory decision making…exciting times!

 

DECEMBER 2021 = EMA – Data Standardisation Strategy [ref 2]

      • The European medicines regulatory network’s data standardisation strategy sets out principles to guide the definition, adoption and implementation of international data standards by the network.
      • It aims to:
        • enable quicker uptake of international data standards across the EU
        • improve data quality
        • enable data linkage and data analysis to support medicine regulation

FEBRUARY 2022 = EMA – Data Analysis and Real World Interrogation Network (DARWIN EU®) [ref 3] [ref 4]

      • In February 2022, EMA selected a service provider (Erasmus University Medical Center Rotterdam) to deliver DARWIN EU
      • By 2024, DARWIN EU will be fully operational with involvement of data partners and medicine regulators
      • The vision of DARWIN EU® is to give EMA and national competent authorities in EU Member States access to valid and trustworthy real-world evidence, for example on diseases, patient populations, and the use, safety and effectiveness of medicines, including vaccines, throughout the lifecycle of a medicinal product
      • The role of the Coordination Centre is to develop and manage a network of real-world healthcare data sources across the EU and to conduct scientific studies requested by medicines regulators and, at a later stage, requested by other stakeholders.
      • By supporting decision-making on the development, authorisation and surveillance of medicines, a wide range of stakeholders will benefit, from patients and healthcare professionals to health technology assessment bodies and the pharmaceutical industry. Additionally, DARWIN EU® will provide an invaluable resource to prepare for and respond to future healthcare crises and pandemics.
      • For example, the availability of timely and reliable real-world evidence can lead to innovative medicines becoming more quickly available to patients. Better evidence also supports more informed regulatory decision-making on the safe and effective use by patients of medicines on the market.  

APRIL 2022 = EMA – Big Data Training Tender – Pharmacoepidemiology and Read-world Evidence curriculum 

The Pharmacoepidemiology and Read-world Evidence curriculum (Lot 2) aims at training regulators and scientists for the collection, analysis, interpretation, and use of observational/real-world data for regulatory evaluation and decision-making on medicinal products. Upon completion of the curriculum, training participants should have a good understanding of the pharmacoepidemiological and real-world evidence concepts and methods to develop, analyse and critically review protocols and reports of non-interventional studies.

MAY 2022 = European Commission – European Health Data Space (EHDS) Proposal [ref 5]

      • Proposal for a European Health Data Space published in May 2022
      • Planned to be effective (in force) by 2025
      • Implements a European electronica health record (EHR) exchange format
      • MyHealth@EU = Empowers individuals through increased digital access to and control of their electronic personal health data, nationally and cross-borders, as well as support to their free movement, fostering a genuine single market for electronic health record systems, relevant medical devices and high-risk artificial intelligence (AI) systems (primary use of data)
      • HealthData@EU = Provides a consistent, trustworthy, and efficient set-up for the use of health data for research, innovation, policy-making and regulatory activities (secondary use of data)

JUNE 2022 = EMA – Metadata List Describing Real World Data [ref 6]

A list of metadata describing real-world data sources and studies is available to help pharmaceutical companies and researchers to identify and use such data when investigating the use, safety and effectiveness of medicines.

      • This metadata list will feed into two future EU catalogues on real-world data sources and studies:
        • The catalogue of data sources will cover information on real-world databases, and is due to replace the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) catalogue in late 2023
        • The catalogue of studies will cover studies performed on the data sources, enhancing and replacing the European Union electronic register of post-authorisation studies (EU PAS Register)

JULY 2022 = EMA – Global Regulators Call for International Collaboration to Integrate Real-World Evidence into Regulatory Decision-Making [ref 7]

      • EMA has endorsed a joint statement calling for international collaboration to enable the generation and use of real-world evidence for regulatory decision-making published today by the International Coalition of Medicines Regulatory Authorities (ICMRA).
      • The use of real-world data and real-world evidence in the development, authorisation and monitoring of medicines to support regulatory decision-making is rapidly increasing. Although real-world evidence can play an important role in bridging knowledge gaps, there are still challenges that need to be addressed, such as heterogeneous data sources across the globe and different levels of quality of the data. Interested parties also need to deal with various processes for data sharing and access.
      • During the COVID-19 pandemic, international medicines regulators and researchers have worked together to establish or reinforce collaboration allowing efficient sharing of data and experience in relation to real-world evidence. They agreed to further such collaboration beyond the pandemic.
      • In their statement, ICMRA members pledge to foster global efforts and further enable the integration of real-world evidence into regulatory decision-making. They identify four focus areas for regulatory cooperation:
        • harmonisation of terminologies for real-world data and real-world evidence
        • regulatory convergence on real-world data and real-world evidence guidance and best practice
        • readiness to address public health challenges and emerging health threats; and
        • transparency

SEPTEMBER 2022 = EMA – A Good Practice Guide for the Use of Real-World Metadata [ref 8] [ref 9]

      • This draft guide aims to help regulators, data holders, researchers, pharmaceutical companies, and other interested stakeholders to use the catalogue of data sources that will replace the currently available ENCePP catalogue.
      • For instance, it provides recommendations on how to identify suitable real-world data sources for studies and describes the required metadata elements.
      • Suggestions for consideration received from industry included:
        • Link and cross-reference the Good Practice Guide with the Data Quality Framework to ensure consistency in the use of concepts (i.e., data quality, data reliability) and terminology
        • Clarify the document’s scope from the geographical (EU vs non-EU) and data source type perspectives
        • Add explanations of the metadata and proposed values for key variables and consider additional metadata such as time lag between the collection and availability of data, more granular detail on laboratory data and inclusion of genomic data sources
        • Consider linking the catalogue to similar initiatives such as EHDEN or EHDS catalogues to harmonise definitions and avoid duplication of effort in providing information to different sources from the data holder point of view

OCTOBER 2022 = EMA – Data Quality Framework for EU Medicine Regulation [ref 10]

      • A draft data quality framework for medicine regulation was available for public consultation until 18 November 2022. 
      • The purpose of this framework was to be applicable for any type of human and veterinary data that might be submitted to medicines regulator. The work will continue next year to apply data quality principles outlined in the framework to specific domains (e.g. real-world, manufacturing etc.) and to ensure alignment with developments coming from the European Health Data Space.
      • This guidance document sets out the criteria for a more consistent and standardised approach to the quality of data used in medicine regulation to support benefit-risk decisions. 
      • RWD quality should be considered in terms of (1) Relevance, (2) Reliability, (3) Extensiveness, (4) Coherence, and (5) Timeliness

NOVEMBER 2022 = EMA – DARWIN EU Data Partners Onboarded [ref 11] [ref 12]

In November 2022, DARWIN EU completed the onboarding of the first set of data partners with access to real-world healthcare data from sources such as hospitals, primary care, health insurance, registries and biobanks.

References

1. EMA – A vision for use of real-world evidence in EU medicines regulation (24 November 2021)

Link: https://www.ema.europa.eu/en/news/vision-use-real-world-evidence-eu-medicines-regulation

2. EMA – Data Standardisation Strategy [17 Dec 2021]

Link: https://www.ema.europa.eu/en/about-us/how-we-work/big-data#data-standardisation-strategy-section  

3. EMA – Initiation of DARWIN EU® Coordination Centre advances integration of real-world evidence into assessment of medicines in the EU

Link: https://www.ema.europa.eu/en/news/initiation-darwin-eur-coordination-centre-advances-integration-real-world-evidence-assessment  

4. EMA – Data Analysis and Real World Interrogation Network (DARWIN EU) 

Link: https://www.ema.europa.eu/en/about-us/how-we-work/big-data/data-analysis-real-world-interrogation-network-darwin-eu   

5. European Commission – Proposal for a regulation – The European Health Data Space (May 2022)

Link: https://health.ec.europa.eu/publications/proposal-regulation-european-health-data-space_en    

6. EMA – Metadata List Describing Real World Data [10 June 2022]

Link: https://www.ema.europa.eu/en/about-us/how-we-work/big-data#metadata-list-describing-real-world-data-section 

7. EMA – Global regulators call for international collaboration to integrate real-world evidence into regulatory decision-making [22 Jul 2022]

Link: https://www.ema.europa.eu/en/news/global-regulators-call-international-collaboration-integrate-real-world-evidence-regulatory-decision  

8. EMA – A Good Practice Guide for the Use of Real-World Metadata [27 Sept 2022]

Link: https://www.ema.europa.eu/en/about-us/how-we-work/big-data#metadata-list-describing-real-world-data-section  

9. EMA Report from the Second bi-annual Big Data Steering Group and industry stakeholders meeting [3 Nov 2022]

Link: https://www.ema.europa.eu/en/events/second-bi-annual-big-data-steering-group-industry-stakeholders-meeting    

10. EMA – Data quality framework for EU medicine regulation [10 Oct 2022]

Link: https://www.ema.europa.eu/en/about-us/how-we-work/big-data    

11. Data Analysis and Real World Interrogation Network (DARWIN EU®) 

Link: https://www.ema.europa.eu/en/about-us/how-we-work/big-data/data-analysis-real-world-interrogation-network-darwin-eu 

12. Data Analysis and Real World Interrogation Network (DARWIN EU®) – Data Partners

Link: https://www.ema.europa.eu/en/about-us/how-we-work/big-data/data-analysis-real-world-interrogation-network-darwin-eu#data-partners-(new)-section  

EU | EMA 2025 Vision for the Regulatory Use of RWE – Connecting the Pieces2023-02-06T12:20:34+00:00

USA | FDA Announces New Funding Opportunity for Using Real-World Data to Generate Real-World Evidence in Regulatory Decision-Making

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USA | FDA Announces New Funding Opportunity for Using Real-World Data to Generate Real-World Evidence in Regulatory Decision-Making2023-01-06T14:48:13+00:00

EU | EMA Publishes Draft Good Practice Guide for the use of the Metadata Catalogue of Real-World Data Sources

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EU | EMA Publishes Draft Good Practice Guide for the use of the Metadata Catalogue of Real-World Data Sources2022-11-14T15:44:52+00:00

RWR Insights | Using RWE to Support Regulatory Decisions – Evidence Considerations and Opportunities

RWR CONTEXT

The healthcare real world evidence (RWE) regulatory framework has rapidly evolved over the past few years (see below), such that RWE is increasingly being used to support and inform the clinical development of medicinal products.

Despite this rapid evolution, the regulatory acceptance of RWE is limited, with the majority of RWE submitted to the EMA being preemptive post-authorisation safety studies (PASS).  Furthermore, FDA acceptance of RWE in the form of external controls is limited by poor data quality, bias and inappropriate use of real world data sources. 

The use of RWD/RWE have become increasingly common and relevant, especially in oncology, because there is a growing recognition that RCTs might not be sufficiently representative of the entire patient population that is affected by cancer, and that specific clinical research questions might be best addressed by RWD/RWE [1].

RWE use cases include:

      • Identify patients and patient subpopulations
      • Population bridging
      • Comparative effectiveness
      • External controls
      • Inform trial design
      • Establish endpoints

The FDA has recently indicated it is aware and supportive of the fact that pharma needs use RWD in drug discovery. The industry now needs to create the interoperability, standards, and methods to ensure that data can be included in regulatory submissions. This evolution may be akin to the critical path initiative. When the FDA embraced the idea of the critical path and allowing more in silico modeling of clinical trial design and development, it took the industry almost 10 years to adopt and apply the guidance [2].

According to the EMA 2025 Vision for RWE Use in EU Medicines Regulation [3]:

“…by 2025 the use of real-world evidence will have been enabled and the value will have been established across the spectrum of regulatory use cases”

Use of RWE to Support Regulatory Decisions – EU

In a recent publication (Flynn et al., 2021) [4], the EMA assessed the contribution of RWE to marketing authorization applications (MAAs) made to the European Medicines Agency in 2018–2019.  

According to Flynn et al., [4] there is widespread use of RWE to support evaluation of MAAs and extension of indications (EOIs) submitted to the EMA.  RWE can have a substantial impact on regulatory decision making, for example by:

      • Informing on the natural history of disease and standards of care
      • Contextualizing results of uncontrolled trials when used as comparator groups of patients for single arm trials, or
      • Collecting follow-up data to generate post-authorization evidence on long-term safety and effectiveness of medicinal products

Interestingly, even though there is widespread use if RWE to support the evaluation of MAAs and EOIs, the majority of uses are still limited to post-authorisation safety studies, as indicated below:

      • 40% of initial marketing authorisation applications and 18% of applications for extension of indication for products currently on the market contained RWE
      • 86% of RWE submitted as part of an MAA were Post-Authorisation Safety Studies (PASS)

DARWIN EU

The EMA  is creating a Data Analysis and Real-World Interrogation Network (DARWIN EU).  The aim of DARWIN EU is to [5]:

      • Deliver real-world evidence from across Europe on diseases, populations and the uses and performance of medicines
      • Enable EMA and national competent authorities to use these data whenever needed throughout the lifecycle of a medicinal product.
      • Support regulatory decision-making by:
          • establishing and expanding a catalogue of observational data sources for use in medicines regulation;
          • providing a source of high-quality, validated real world data on the uses, safety and efficacy of medicines;
          • addressing specific questions by carrying out high-quality, non-interventional studies, including developing scientific protocols, interrogating relevant data sources and interpreting and reporting study results.

DARWIN EU will support the implementation of the European Health Data Space (EHDS), which aims to leverage EU-wide health data for (amongst other things) research purposes.

International Collaboration to Integrate Real-World Evidence into Regulatory Decision Making

Recently (July 2022), the EMA, FDA and Health Canada published a statement on international collaboration to integrate real-world evidence into regulatory decision-making [6].  

According to the statement, the following collaboration opportunities exist:

      • Harmonisation of RWD and RWE terminologies:
          • Generate common operational definitions of RWD and RWE, with clear scope and level of
          • granularity (e.g., pertaining to RCTs and observational studies)
          • Leverage existing ICH activities, such as M14 on “General principles on planning and
          • designing pharmacoepidemiological studies that utilize real-world data for safety assessment of a medicine”
      • Convergence on RWD and RWE guidance and best practice, including:
          • Common principles for RWD quality;
          • Metadata to enable characterisation and discoverability of RWD
          • Suitable scenarios where RWE may contribute to regulatory decision-making, building on
          • existing use-cases
          • Templates for study protocols/reports that can be used in multiple regulatory jurisdictions
      • Readiness
          • Through the strengthening of international regulatory collaboration on RWE, enable the rapid creation of expert groups on specific topics of interest, including in case of emerging health threats
          • Foster collaboration on governance and processes to enable the efficient conduct of studies based on RWD from different countries to address important public health challenges
      • Transparency
          • Define common principles and practices for the systematic registration of pre-specified study
          • protocols (including description of feasibility assessments) and study results in publicly
          • available registries
          • Promote publication of study results in open-source, peer-reviewed journals

NICE (UK) RWE Framework 2022 

NICE makes recommendations that guide decisions in health, public health, and social care.  We want to make greater use of real-world data to learn from the delivery of health and social care and patient health and experiences to improve these recommendations [7].

According to NICE’s RWE Framework, randomised controlled trials (RCT) re the preferred source of evidence on the effects of interventions. However, randomised trials are sometimes unavailable or are not directly relevant to decisions about patient care in the NHS, for reasons including [7]:

      • Randomisation is considered unethical, for instance because of high unmet need 
      • Patients are unwilling to be allocated to one of the interventions in the trial 
      • Healthcare professionals are unwilling to randomise patients to an intervention which they consider less effective 
      • A small number of eligible patients 
      • Financial or technical constraints on studies 
      • Not all treatment combinations (including treatment sequences) can be directly assessed

Even if randomised evidence is available, it may not be sufficient for decision making in the NHS for several reasons including [7]: 

      • The comparator does not reflect standard of care in the NHS 
      • Relevant population groups are excluded 
      • There are major differences in patient behaviours, care pathways or settings that differ from implementation in routine practice 
      • Follow up is limited 
      • Unvalidated surrogate outcomes are used 
      • Learning effects are present 
      • Trials were of poor quality

Real-world data is already widely used to inform NICE guidance to, for example [7]: 

      • Characterising Health Conditions and and Patient Outcomes – characterise health conditions, interventions, care pathways and patient outcomes and experiences 
      • Economic Models – design, populate and validate economic models (including estimates of resource use, quality of life, event rates, prevalence, incidence and long-term outcomes) 
      • Digital Health Technologies – develop or validate digital health technologies (for example, digital technologies may use a clinical algorithm developed using real-world data) 
      • Health Inequalities – identify, characterise and address health inequalities 
      • Safety – understand the safety of medical technologies including medicines, devices and interventional procedures 
      • Impact Assessments – assess the impact of interventions (including tests) on service delivery and decisions about care 
      • Trial Data Generalisability/ Relevance – assess the applicability of clinical trials to patients in the NHS. 

Real-world data that represents the population of interest is NICE’s preferred source of evidence for most of these applications. Such data is regularly used for these purposes in NICE guidance, but its use could be more commonplace, especially of routinely collected data. Real-world data could be used more routinely to fill evidence gaps and speed up patient access. For this promise to be realised, real-world evidence studies must be performed transparently and with integrity, use fit-for-purpose data, and address the key risks of bias. 

Accordingly, NICE are communicating their view on best practices for the conduct of real-world evidence studies to ensure they are generated transparently and are of good quality. This is essential to improving trust in real-world evidence studies and their use in decision making. 

External Controls

Real-world evidence (RWE) is increasingly being used to support and inform the clinical development of medicinal products. RWE use cases include external controls.

An external control arm is an umbrella term referring to any control that is not a randomized control and includes control data collected concurrently or historically from previous clinical trials or RWD [8].

External Controls – Uses and Trends [9]:

      • Accelerated approval programs for high morbidity and high unmet need diseases have driven the use of single-arm studies, studies that do not include placebo or active comparator arms (ie, no concurrent control), for drug development. 
      • Single-arm studies speed up patient access to innovative treatments because they often require fewer patients than randomized controlled trials (RCTs) and use intermediate or surrogate endpoints (eg, objective response rate [ORR]). 
      • Oncology drug development has been increasingly relying on single-arm studies: from 1992 to 2017, 67% of the Food and Drug Administration’s (FDA) accelerated approvals were based on single-arm trials.1 
      • Similar trends have been observed in health technology assessment (HTA) submissions. From 2000 to 2016, 22 submissions to the National Institute for Health and Care Excellence (NICE) in the UK were based on nonrandomized data, and oncology drugs accounted for more than half of these submissions. 
      • A consequence of using single-arm studies is generally less evidence on the therapeutic benefits of the product at market launch [Drives the need for confirmatory trials post-approval]. 
      • In the absence of the preferred evidence standard (i.e., large-scale RCTs with a true state of clinical equipoise) in submission packages, real-world evidence (RWE)—or evidence generated from real- world data (RWD)—is increasingly being considered by decision makers. 
      • External control arms (ECAs) generated from RWD, or “data relating the patient health status and/or the delivery of healthcare routinely collected” from insurance claims, electronic health re- cords (EHRs), registries, etc, are emerging to contextualize single-arm trial data by exploring what would happen if single-arm trial patients did not receive the study drug. 

External Controls – Challenges [8]:

      • There are numerous challenges to decision makers’ adoption of RWE, especially in the context of ECAs, as a trusted source of evidence. One main concern is the potential to introduce bias that is often reduced through randomization 
      • 2 common methodological challenges are:
          • Confounding = Systematic differences in patient characteristics across treatment groups that can affect effectiveness
          • Selection Bias = Individuals in a study differing systematically from the population of interest, leading to a systematic error in an association or outcome

The FDA has said it recognizes the importance of RWD, but that acknowledgement has resulted in few approvals. Looking at the use of synthetic control arms and RWD in regulatory submissions over the last five years shows just 10 submissions and all were in oncology. Only one was a successful submission, and the rest were rejected because of lack of completeness of the data [2].

According to the FDA review of the External Control for Xpovio (selinexor) [9]:

 “Due to major methodological issues (including immortal time bias, selection bias, misclassification, confounding, and missing data), the FDA does not consider these results adequate to support regulatory decision making.” (2019 NDA Review)

External Control – Rozlytrek (Entrectinib)  – List of key study limitations identified by the FDA [10] [11] [12]:

      • Post-hoc analysis (lack of prior FDA review of the protocol and SAP)
      • Selection Bias
      • Missing Data Among Covariates and Missing Covariates
      • Statistical Modeling…limited by sample size
      • Measure of Study Outcomes – Not adequate to allow a robust comparison of treatment outcomes between arms

Impact = Label limitations (FDA label of Rozlytrek excluded TTD, PFS, and OS outcomes, and only referenced improvements in ORR) [12].

External Control – Abecma (idecabtagene vicleucel/ide-cel) – List of Key Issues Identified by the FDA [13]:

      • Selection criteria issues
      • Missing data
      • RWD not fit for purpose
      • Impact
      • ECA not used
      • Systematic literature review accepted but of ‘limited utility’ due to lack of patient-level data

FDA Summary = “Given the methodological limitations discussed above, we conclude that the evidence generated from the RW analysis is not adequate to provide context or comparison for the outcome of MM-001 study” [14]

FDA feedback on the systematic literature review: “Given the differences in the patient characteristics, the definitions of outcomes, and other unreported differences across the studies, the treatment effect estimate may be biased limiting the utility of this analysis” [15]

External Control – Abecma (idecabtagene vicleucel/ide-cel) – List of Key Issues RWE Study Identified by the EMA [16]:

      • The rather long time period (up to 60 days from the index date) allowed for the collection of baseline data
      • The overlapping recruitment periods for the RWS and the MM-001 at the same study centres
      • The large proportion of missing data (up to 30%) for some included co-variates
      • Several co-variates excluded from the PS model due to >30% missing data

According to the EMA, despite the limitations of the indirect treatment comparisons, the results indicate that ide-cel treatment is associated with responses that are well above those reported with current standard of care [16].

Summary of FDA Findings/Limitations for Reviewed External Controls 

ECA evidence appears to have limited impact on regulatory and HTA decision making [8].

List of key external control limitations identified by the FDA:

      • Post-hoc analysis (lack of prior FDA review of the protocol and SAP)
      • Selection Criteria Issues (selection bias, misclassification, and confounding)
      • Index Date Issues (immortal time bias)
      • Comparability Issues (lack of comparability, missing data, confounding bias)
      • Limited Sample Size (Lack of statistical power due to insufficient real world cohort size)
      • RWD not fit for purpose

Suggestions for optimising the acceptability of external controls for regulatory decision making:

      • Design the external control to emulate the preferred randomised controlled trial – use a “target trial approach”
      • Use appropriate RWD sources to optimise comparability, relevance and generalizability of the control group population
      • Seek early FDA review of the protocol and SAP (a priori rather than post hoc)
      • Assess and limit bias using the appropriate methodological/ statistical designs
      • Use the START-RWE templates to demonstrate the what, how and why of the data sources, curation and analysis
      • List the limitations of the RWD/ RWE in the protocol

Using RWE to Support Regulatory Decisions – Evidence Considerations and Opportunities

The healthcare real world evidence (RWE) regulatory framework has rapidly evolved over the past few years (see below), such that RWE is increasingly being used to support and inform the clinical development of medicinal products. RWE use cases include:

      • Identifying patients and patient subpopulations
      • Population bridging
      • Comparative effectiveness
      • External controls
      • Informing trial design
      • Establishing endpoints

Despite this rapid evolution, the regulatory acceptance of RWE is limited, with the majority of RWE submitted to the EMA being preemptive post-authorisation safety studies (PASS).  Furthermore, FDA acceptance of RWE in the form of external controls is limited by poor data quality, bias and inappropriate use of real world data sources. 

Moving forward, real-world evidence studies need to be conducted so that they are transparent and reproducible and the data generated (RWE) are relevant, reliable and of good quality. This is essential to improving trust in real-world evidence studies and their use in regulatory decision making.

Much progress has been made since 2016…it is clearly evident that there is significantly more work needed before the RWE generated during drug development are of acceptable quality and reliability to support MAA, NDA and BLA submissions.

References

1. Batra A, Cheung WY. Role of real-world evidence in informing cancer care: lessons from colorectal cancer. Curr Oncol. 2019 Nov;26(Suppl 1):S53-S56. doi: 10.3747/co.26.5625. Epub 2019 Nov 1. PMID: 31819710; PMCID: PMC6878934.

Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6878934/ 

2. The Next Three Years Of Clinical Trials: DCTs, RWE, And Beyond. Ed Miseta. Clinical Leader (18 April 2022)

Link: https://www.clinicalleader.com/doc/the-next-three-years-of-clinical-trials-dcts-rwe-and-beyond-0001 

3. EMA – A vision for use of real-world evidence in EU medicines regulation (24 November 2021)

Link: https://www.ema.europa.eu/en/news/vision-use-real-world-evidence-eu-medicines-regulation  

4. Flynn, R., Plueschke, K., Quinten, C., Strassmann, V., Duijnhoven, R.G., Gordillo-Marañon, M., Rueckbeil, M., Cohet, C. and Kurz, X. (2022), Marketing Authorization Applications Made to the European Medicines Agency in 2018–2019: What was the Contribution of Real-World Evidence?. Clin. Pharmacol. Ther., 111: 90-97. https://doi.org/10.1002/cpt.2461

Link: https://ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.2461  

5. EMA – Data Analysis and Real World Interrogation Network (DARWIN EU)

Link: https://www.ema.europa.eu/en/about-us/how-we-work/big-data/data-analysis-real-world-interrogation-network-darwin-eu   

6. EMA – Global regulators call for international collaboration to integrate real-world evidence into regulatory decision-making

Link: https://www.ema.europa.eu/en/news/global-regulators-call-international-collaboration-integrate-real-world-evidence-regulatory-decision  

7. NICE Real-World Evidence Framework – Corporate document [ECD9]Published: 23 June 2022

Link: https://www.nice.org.uk/corporate/ecd9/chapter/overview  

8. Ashley Jaksa, Anthony Louder, Christina Maksymiuk, Gerard T. Vondeling, Laura Martin, Nicolle Gatto, Eric Richards, Antoine Yver, Mats Rosenlund. A Comparison of Seven Oncology External Control Arm Case Studies: Critiques From Regulatory and Health Technology Assessment Agencies. Value in Health, 2022. ISSN 1098-3015, doi.org/10.1016/j.jval.2022.05.016. (25 June 2022)

Link: https://www.sciencedirect.com/science/article/pii/S1098301522020046  

Link2: https://www.valueinhealthjournal.com/article/S1098-3015(22)02004-6/fulltext?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1098301522020046%3Fshowall%3Dtrue   

9. Section 7.2.6 (KS-50039 (Retrospective observational study using real-world data)) (page 84) – FDA – NDA/BLA Multi-disciplinary Review and Evaluation, NDA 212306, XPOVIO® (selinexor) (July 2019) 

Link: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212306Orig1s000MultidisciplineR.pdf 

10. FDA approves entrectinib for NTRK solid tumors and ROS-1 NSCLC (August 2019) 

Link: https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-entrectinib-ntrk-solid-tumors-and-ros-1-nsclc  

11. Drugs@FDA: FDA-Approved Drugs – Rozlytrek (NDA 212725)

Link: https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm?event=overview.process&ApplNo=212725 

12. Section 8 – Appendix (page 30) – CDER – Review of Study Report No WO40977: Comparative analysis of ROS1-positive locally advanced or metastatic non-small cell lung cancer between patients treated in entrectinib trials and crizotinib treated patients from real world data (11 July 2019)

Link: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212725Orig1s000,%20212726Orig1s000OtherR.pdf  

13. Section 2.5 (Summary of Pre- and Post-submission Regulatory Activity Related to the Submission)(page 16) – FDA BLA Clinical Review Memorandum – STN 125736/0 (27 July 2020)

Link: https://www.fda.gov/media/147740/download 

14. Section 9.2 (Aspect(s) of the Clinical Evaluation Not Previously Covered- Systemic Literature Review/ Available therapies)(page 115) – FDA BLA Clinical Review Memorandum – STN 125736/0 (27 July 2020)

Link: https://www.fda.gov/media/147740/download 

15. Section 9.2 (Aspect(s) of the Clinical Evaluation Not Previously Covered- Systemic Literature Review/ Available therapies)(page 115) – FDA BLA Clinical Review Memorandum – STN 125736/0 (27 July 2020)

Link: https://www.fda.gov/media/147740/download 

16. Section 2.5.3 (Discussion on clinical efficacy) (page 113) – EMA – Abecma – EPAR

Link: https://www.ema.europa.eu/en/medicines/human/EPAR/abecma 

RWR Insights | Using RWE to Support Regulatory Decisions – Evidence Considerations and Opportunities2022-09-12T11:37:21+00:00
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