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, 2018 JSM, Vancouver

FDA CDRH Risk-Based Device Regulations www.fda.gov

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 www.fda.gov

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 www.fda.gov

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 www.fda.gov https://go.usa.gov/xUpzX

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 www.fda.gov

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 www.fda.gov

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 www.fda.gov

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

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

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 https://go.usa.gov/xUVdn Source: Yue 2018 www.fda.gov

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 https://go.usa.gov/xUVdU; https://go.usa.gov/xUVdE; https://go.usa.gov/xUVdm www.fda.gov

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)

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

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

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

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, 1136-1145. 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 https://go.usa.gov/xUpzX. 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.

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