1. Opportunities of Using Real World Data to Inform Regulatory Considerations for Medical Devices
Martin Ho
Associate Director for Quantitative Innovations
Center for Devices and Radiological Health, U.S. FDA
August 1, JSM, Vancouver

2. FDA CDRH Risk-Based Device Regulations

3. Medical Device Regulatory Decisions
Investigational Device Exemption
Safe for human experiments?
Early feasibility studies
Exploratory studies with small number of subjects guiding pivotal study design
Pivotal studies
Endpoints and sample size statistically driven
Designed to assess both safety and effectiveness
Pre-Market Approval or Humanitarian Device Exemption
Reasonable assurance of safety and effectiveness
Claims in label supported by evidence

4. RWD vs Traditional RCTs
RWDs
Benefits
Bias & confounders mitigated
Limitations
Generalizability
Ethics of randomization & control treatments
Cost (e.g., rare endpoints)
Benefits
Real world effectiveness
Limitations
Bias & confounders
Missing data & censoring
Different work flow & practice
Diverse & large population

5. FDA Medical Device Guidance on RWE
Real-World Data (RWD)
Data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources
RWD
RWE
Analysis
Collection
Use
Real-World Evidence (RWE)
Clinical evidence regarding the usage & potential benefits or risks of a medical product derived from analysis of RWD

6. Patient Journey and Various RWD Sources
Onset
Receive Treatment
Recovery (Outcome)
EHR/EMR What providers saw and did
Claims What providers charge
Registry Summary from onset to recovery
Digital Health Patient-generated data collected from mobile health devices

7. Heterogeneous RWD Sources Means Great Research Opportunities
Feature (Opportunity)
Registries
Observational data (selection bias, confounding, missing data)
EHR / EMR
Provider records (censoring as patients switch providers)
Pragmatic trials
Providers as experimental units (cluster random. & analysis)
Claim-based data
Coding driven events (censoring, coding error/ triangulation)
Pt.-generated data
Different algorithms & PROMs (measure. validity & reliability)
Passive & centralized operation: adjudicate and combine for specific endpoints
A better & proactive way: Distributed Data Network

8. NEST: Network of RWD Stakeholders
Professional Societies /
Researchers
Registries
Natural history studies
Pragmatic trials
Care providers
EHR / EMR
Labs / Pharmacy
Payors, CMS
Claim-based admin. data
Patients, vendors
Patient-generated data reported by mobile devices
National Evaluation System for health Technologies (NEST)
NEST
CDRH
Hospital Systems
Patient Groups
Clinician Groups
Payers
Industry
NESTcc

9. RWD Inform Device Regulatory Decisions
Pre-market
Post-market
Investigational devices
Inform pivotal study design
Provide supportive data
“Off-label” to expand label
Shift pre-post market data
Devices on market
Real-life performance
Long-term outcomes
Larger & diverse population
Safety surveillance update

10. Examples of RWE-informed Device Regulatory Decisions
Wide device spectrum
Examples
Cardiovascular devices
Orthopedic devices
Neurological devices
Surgical devices
In vitro diagnostic tests
Cardiac device – National registry
Sequencing assay – Public NGS DB

11. Ex 1. Use Registry for IFU Expansion
Investigational device – Percutaneous transluminal angioplasty Drug-coated Balloon Catheter
Study design – Comparative study for pre- market approval of an indication expansion
RWD source – Society for Vascular Surgery Vascular Quality Initiative, a national device registry
Use of RWD – External control group for the PMA/S approval and post-market surveillance
Statistical method – Propensity score
Source: Yue 2018

12. Ex 2. Use Public Database for Pre-market Claims
Two sequencing assays cleared for variants/variant combinations associated with cystic fibrosis using a public next-generation sequencing (NGS) database
Traditional evidence: Full clinical trials/summary of information available in peer-reviewed literature to provide evidence of the test’s clinical validity.
RWD source – An established publicly-maintained database hosted by the academic institution
Use of RWD – Valid evidence to establish which variants/ variant combinations were causal for the target disease
Source: Yue 2018

13. A Popular RWE Tool: Propensity Score
Propensity score commonly used to compare patient outcomes in historical control (registries) and prospective single-arm investigational device studies
Prospective “2-stage design”1
1: Outcome-blinded sample size estimation2
2: Outcome-blinded propensity score modeling, followed by unblinded comparative analysis
Data quality & relevance also critical for validity
1 Yue et al. (2016)
2 Yue et al. (2014)

14. New Data Sources, New Methods: Fit for Regulatory Purposes
Data Quality & Relevance
Statistical Methods
What types of reg. questions can be informed by RWD?
What kinds of RWD can answer diff. types of reg. Q’s?
How to evaluate RWD’s quality & relevance for diff. purposes?
How to build a RWD network to generate reg. evidence?
Like vs Like* or fruit basket?
How to combine data of different sources & natures?
How to assign relative weights to data from studies with different design?
How RWD can improve RCT?
Pragmatic trials

15. Some Ongoing RWE Initiatives
ASA BIOP Section RWE SWG
Other
Statistical, Regulatory Focus
Efficacy & Safety
Statistics, AI, and ML
Inaugural meet Vancouver!
First 2 Workstreams:
RWE for label expansion
RWE to improve RCT design
ASA BIOP Safety SWG
Sentinel
NEST
PCORNet
IMI GetReal
FDA CDRH Digital Health Prg

16. Summary
Relevant & high quality RWE can fill the gaps of traditional clinical trials
Statistical methods (e.g., propensity score) have been used by FDA for regulatory decisions
Emergent types of RWD require new methods
Exciting opportunities await statisticians and efforts are underway

17. Reference
Yue, L.Q., Campbell, G., Lu, N., Xu Y., Zuckerman, B. (2016) Utilizing national and international registries to enhance pre-market medical device regulatory evaluation, Journal of Biopharmaceutical Statistics, 26:6,
Yue, L. Q., Lu, N., Xu, Y. (2014) Designing premarket observational comparative studies using existing data as controls: challenges and opportunities. Journal of Biopharmaceutical Statistics 24(5):994–1010.
U.S. FDA (2017) Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices: Guidance for Industry and Food and Drug Administration Staff. Available at
Yue, L. Q., Dumont, D., Waters, M. (2018) Leveraging Real-World Data in Medical Device Evaluation: from Theory to Practice. 11th Annual FDA-AdvaMed/MTLI Statistics Workshop, Washington D.C.

18. Acknowledgement
Many thanks for Lilly Yue, Ph.D., Douglas Dumont, Ph.D., and Michael Waters, Ph.D., for allowing me to use some of their case slides for this presentation