Medical Device Clinical Studies and Protocol Design IVT Medical Device Conference San Francisco

Medical Device Clinical Studies andProtocol Design IVT Medical Device Conference San Francisco August 17, 2006 Michael A. Swit, Esq. Vice President, Life Sciences

Presentation Overview • Standards of Approval – What the Protocol Targets • Key Considerations in Designing Clinical Studies • Practical Lessons in Clinical Trial Design & Execution

Approval Standard … PMA • PMA Approval Standard – “reasonable assurance that the device is safe and effective under the conditions of use prescribed, recommended, or suggested in the labeling” • “Valid Scientific Evidence” – FDA relies “only” on to determine reasonable assurance – 21 CFR 860.7(c)(1)

Approval Standard … PMA • “Valid scientific evidence” = evidence from well-controlled investigations, partially controlled studies, studies and objective trials without matched controls, well-documented case histories conducted by qualified experts, and reports of significant human experience with a marketed device, from which it can be fairly and responsibly concluded by qualified experts that there is a reasonable assurance of the safety and effectiveness of a device under its conditions of use. source: 21 CFR 860.7(c)(2).

“Clearance” Standard … 510(k) • "substantially equivalent" or "substantial equivalence" means, with respect to a device being compared to a predicate device, that the device has the same intended use as the predicate device and that the Secretary by order has found that the device— • (i) has the same technological characteristics as the predicate device, or • (ii)(I) has different technological characteristics and the information submitted that the device is substantially equivalent to the predicate device contains information, including appropriate clinical or scientific data if deemed necessary by the Secretary or a person accredited under section 523, that demonstrates that the device is as safe and effective as a legally marketed device, and (II) does not raise different questions of safety and effectiveness than the predicate device.

“Conformity Standard” for CE Marking in the EU • Remember – unlike with pharmaceuticals, there is no pre-market role for devices either at an EU centralized authority (indeed, unlike drugs, there is no central authority for devices) or in member states • CE Marking – handled via reviews by “notified bodies” • Must meet applicable Device Directive • Implantable • Non-Diagnostic • Diagnostic

Background … 510(k) Studies • Substantial Equivalence clinical studies are only required in ~10% of all Class II 510(k) submissions • When no reliable method is available to validate substantial equivalence to a predicate device • Product-related issues • Novel design • New technology • New indications for use • Upon request by FDA

Background … 510(k) Studies • Superiority and economic data not required for FDA clearance of a 510(k) submission. . . . . .but these data are required to support reimbursement applications with CMS or private payers • Data to support FDA clearance may not be the data needed for reimbursement; marketing goals

Background … 510(k) Studies • What is an equivalence trial? “…a clinical trial designed to evaluate whether an experimental treatment E is similar to a control treatment S, by an appropriate definition of similarity…” Reference: W.C. Blackwelder, 2004, J. Dent. Res. 83

Background … 510(k) Studies • Equivalence • Two-sided or bi-directional • e.g. pharmaceutical bio-equivalence • Non-inferiority • One-sided or uni-directional • e.g. most 510(k) clinical equivalence studies NOTE: Equivalence and non-inferiority are similar but not the same thing….most 510(k) ‘substantial equivalence’ trials are technically ‘non-inferiority’ trials

Regulatory Considerations … 510(k) Studies • Remember – prime focus of 510(k) is substantial equivalence • So, how do you know you need a clinical study? • Comparison to predicate – technology • Comparison to predicate – intended use • Comparison to requirements for similar devices • Any factor raising new questions of safety or effectiveness that cannot be alleviated through bench testing points towards a clinical trial

Regulatory Considerations … 510(k) Studies • Communicating with FDA • When – if the path is not evident • Recommend an informal “guidance” meeting • Requires meeting request letter, preparation of pre-meeting package with pointed questions regarding strategy • Maximizing your chances of a positive outcome – organized, complete, concise package and well constructed strategy and questions for discussion

Regulatory Considerations … 510(k) Studies • Case Studies • Clinical data probably required • Non-invasive blood glucose meter • New or significant change of clinical instrument software algorithms • Thermal regulation catheter system • Clinical data probably not required • New LED vendor for existing pulse oximeter sensor • Data interface for fingerstick blood glucose meter

Different Clinical Study Hurdles For Different Audiences Effectiveness Reimbursement “Code” Pricing Safety Efficacy Prescriber Patient “End-user” “Payer” “Regulator” Adapted from Perfetto 2001

Regulatory Considerations – Reimbursement & Studies • CMS -- primary focus is effectiveness • Requires systematic evaluation of the performance and properties of the technology: • All available clinical and outcomes data • Comprehensive review of relevant literature (published and unpublished) • If marketed, opinions/data from leaders in the field on real-world use • Analysis of competitive advantage • Overall economic impact including costs offsets

Regulatory Considerations – Reimbursement & Studies • Data needs for reimbursement purposes often go beyond what is required to fulfill regulatory requirements • To the extent possible, requisite data should be determined as part of the medical devices’ product development plan • Different types of studies could be required at different times throughout the process • A strategy for data collection needs to be started early

Regulatory Considerations – Reimbursement & Studies • Avoid misconceptions such as: • It is “soft” science,so it doesn’t need much attention • Nothing can be done until you are close to marketing • It can be done quickly • Piggybacking is all that is needed • “This is a drug thing”:biotech, genomic, and device products do not need to worry about this

Trial Design – Key Issues • Trial Goal or Objective -- clinical endpoints • Pilot or Feasibility Study • Evidence to support trial • Identification & Selection of Variables • Confounding variables • Study Population • Control Population • Methods of Assigning Interventions

Trial Design – Key Issues … • Masking • Trial Site and Investigator • Bias • Sample Size and Statistical considerations • Design challenges • Key elements of trial design

Trial Goal – i.e., the Research Question • Clinical Goal – measured via endpoints • Primary endpoint should be clinically relevant; objective; measurable with known precision • Secondary endpoints • Support marketing/reimbursement claims, • confirm cost-effectiveness, etc. • Comparative claims • Safety • Caveat -- don’t lose sight of the primary focus, which is data to support a regulatory filing!!

Evidence – Qualitative Hierarchy • Evidence to support clinical trials (in order of quality): • Systematic review of randomized, controlled trials • Randomized, controlled trials • Prospective studies • Retrospective studies • Cross-sectional surveys • Case series • Case reports

Pilot (a.k.a. Feasibility) Studies • Done when a sponsor can not answer key questions that would allow them to focus a clinical trial • Used to • Identify possible medical claims • Monitor potential study variables • Test study procedures (e.g., logistics) • Refine the device prototype itself • Determine precision of potential response variables • Refine the protocol for a future pivotal study • FDA – will expect you to do under an IDE

Observation Variables – Identification & Selection • Two types – “outcome” and “confounding” • Outcome – those that define and answer the research question – a.k.a. endpoints – should be: • Directly observable • Objectively measured • Relate to the biological effects of the clinical condition (which may need to be validated) • Example – if a device reduces a particular blood value, must validate that the blood value is clinically meaningful relative to the condition that will be in the device’s labeling

Variables -- Confounding • Confounding variables • A factor associated with both the outcome measure and the variable of interest (e.g., patient) • Example – a study of blood pressure treatment might be confounded if there were more young people in one arm of the study as younger people simply tend to have lower blood pressure • No study is entirely free of confounders • Methods to reduce confounding • Inclusion/exclusion criteria • Randomization • Statistical measurement methods – quantitative vs. qualitative

Study Population • Key consideration – balancing homogeneity of population vs. heterogeneity • Homogeneous • Advantage – more precision in study measurs • Disadvantage – may limit your “intended use” to a smaller population • Heterogeneous • Advantage – broader label claim • Disadvantage – harder to prove

Control Population • Control – is either real or implicit – must match the study group • Types • Concurrent – assigned an alternative intervention (e.g., placebo or standard of care) • Cross-over – “self” control • Historical – less reliable – requires extensive validation – may be appropriate when a concurrent control might be unethical • Passive concurrent – are not under the direct care of the principal investigator

Ways to Assign Interventions • How done is key to minimizing selection bias • Randomization – key way to decrease – patients assigned to treatment or control in a way that they have an equal chance of ending up in either • But, make sure is truly random – e.g., if you chose every third subject to come in to clinic, might be impacted by external variables that are varying the way folks are coming into the clinic (e.g., seasonality)

Blinding or Masking • Goal – to reduce investigator bias, evaluator bias and placebo effect • QOL measures – are particularly subject to evaluator bias • Single – patient “blind” • Double – patient & investigator “blind” • Third party – evaluator blind (e.g., x-ray reader) • Code – not broken until analysis is done • Challenge – difficult to blind when a device is used (as opposed to drug trials)

Trial Site and Investigator • Selection is key to success – because pooling of data is usually required due to lack of patients • Pre-qualify that there really are patients there • Devices Center – often will regard disqualified subjects as “failures” under an intent to treat approach • Thus, investigator compliance with protocol is key

Bias • Bias – when a characteristic of the study interferes with the ability to measure a variable accurately • is a source of systematic error in a study; it does not occur by chance • may occur at each stage: design; conduct; analysis • is a common problem in reports of clinical experience with devices • Types of Bias -- • Observer bias • Selection bias • Recall bias • Reporting bias • Placebo response • Learning

Sample Size & Statistical Considerations • Sample Size Calculation • The larger the effect size, the smaller the trial • Longer trials require more subjects • Device trials are usually “not worse than” studies (vs. equivalence; superiority) • Typical standards: Power = 80%; p=0.05 • Statistical plan • In writing and in advance! • Clear statistical tests that are consistent with the scientific questions • Provision for post hoc analysis • Bayesian Statistics – use may allow you to cut # of patients or use historical controls (yours or literature) • Guidance of Use of Bayesian Statistics in Medical Device Clinical Trials, May 23, 2006 -- www.fda.gov/cdrh/osb/guidance/1601.pdf

Practical Considerations • Cost of trial vs. market potential • Blinding, randomization often difficult or impossible • Investigator/user skill variability • Double blinding is often not possible • Large trials often not feasible • Pre-clinical data may not predict human experience or failure modes

Practical Considerations -- Execution • Regulatory compliance • NSR vs. SR study (SR study requires IDE) • Good Clinical Practices • e.g., Informed Consent, IRB approval, clinical protocol, clinical trial and data monitoring • If not done right, can invalidate data at site • Monitoring (BIMO) issues • Data analysis according to plan

Practical Considerations -- Data Analysis • Key steps • Complete enrollment • Audit data • Database lock • Primary statistical analysis per plan • Post hoc analysis • Prepare formal report (either internal or for FDA) • Prepare manuscript for publication

Practical Considerations -- Data Analysis … • Methods to Reduce Bias • Weight of the evidence • Consistency • Plausibility • Temporality • Mechanism of Action • Magnitude of effect • Methods to Reduce Confounding • Data stratification: separate data by confounders; relies on clinical judgment, information; and suspicion • Statistical modeling: use correlation and regression methods, often complex

Practical Considerations – What Goes in the Actual Protocol • Background of trial – previous studies on device • Clear statement of trial goals – i.e., endpoints • Complete description of trial design • Design type • Data collection methods • Control type • Blinding parameters • Sample size justification • How treatment group assigned

Practical Considerations – What Goes in the Actual Protocol … • Complete description of study population • Study sites • Selection methods – inclusion/exclusion criteria • Type of patient (inpatient v. outpatient) • Clinical and demographic characteristics of subjects • Complete description of intervention • Frequency & duration of application • Compliance measures – investigator & patient

Practical Considerations – What Goes in the Actual Protocol … • Complete description of follow-up visits • All measures made and info to be collected • How patient withdrawal to be handled • How sponsor will follow up on patient’s health if they drop out • Details on data gathering and analysis • Data collection and validation methods • Data Monitoring • Statistical analysis methods • Specific rules on how/why study can be ended early – use of DMC’s

Practical Considerations – What Goes in the Actual Protocol … • Full info on investigators • CV’s • Monitoring methods • Administration of trial, including how to adjust protocol • Glossary of relevant terms • Informed Consent

References • Statistical Guidance for Clinical Trials of Non-Diagnostic Medical Devices, FDA, Center for Devices & Radiological Health. www.fda.gov/cdrh/ode/ot476.html • Guidelines on Medical Devices – Evaluation of Clinical Data: A Guide for Manufacturers and Notified Bodies, European Commission, Enterprise Directorate General. ec.europa.eu/enterprise/medical_devices/meddev/2_7.pdf

Questions? Call, e-mail, fax or write: Michael A. Swit, Esq. Vice President, Life Sciences THE WEINBERG GROUP INC. 336 North Coast Hwy. 101 Suite C Encinitas, CA 92024 Phone 760.633.3343 Fax 760.633.3501 or 760.454.2979 (preferred) Cell 760.815.4762 D.C. Office 202.730.4123 michael.swit@weinberggroup.com www.weinberggroup.com

About the speaker … Michael A. Swit, Esq., who is Vice President, Life Sciences at THE WEINBERG GROUP INC., has extensive experience in all aspects of FDA regulation with a particular emphasis on drugs and medical device regulation. In addition to his private legal and consulting experience, Mr. Swit also served for three and a half years as vice president and general counsel of Pharmaceutical Resources, Inc. (PRI) a prominent generic drug company and, thus, brings an industry and commercial perspective to his representation of FDA-regulated companies. While at PRI from 1990 to late 1993, Mr. Swit spearheaded the company’s defense of multiple grand jury investigations, other federal and state proceedings, and securities litigation stemming from the acts of prior management. Mr. Swit then served from 1994 to 1998 as CEO of Washington Business Information, Inc. (WBII) a premier publisher of FDA regulatory newsletters and other specialty information products for the FDA publishing company. Before joining THE WEINBERG GROUP, he served in the FDA Regulatory Law Practices at both Heller Ehrman and McKenna & Cuneo, first in that firm’s D.C. office and then in its San Diego office. He first practiced FDA regulatory law with the D.C. office of Burditt & Radzius from 1984 to 1988. Mr. Swit has taught and written on a wide variety of subjects relating to FDA law including, since 1989, co-directing a three-day intensive course on the generic drug approval process, serving on the Editorial Board of the Food & Drug Law Journal, and editing a guide to the generic drug approval process, Getting Your Generic Drug Approved, published by WBII. Mr. Swit holds an A.B., magna cum laude, with high honors in history, in 1979, from Bowdoin College, and earned his law degree from Emory University in 1982. He is a member of the California, Virginia and District of Columbia bars.

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Medical Device Clinical Studies and Protocol Design IVT Medical Device Conference San Francisco