The document discusses the design and regulation of clinical studies for medical devices, highlighting key approval standards such as PMA and 510(k). It emphasizes the importance of substantial equivalence, the types of clinical trials required, and the role of evidence in determining safety and effectiveness. Practical considerations for trial execution, data analysis, and regulatory compliance are also outlined, alongside the need for a well-structured trial protocol.

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

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

3. 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)

4. 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).

5. “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.

6. “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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. PatientPrescriber
“Payer”“Regulator”
Safety Efficacy
PricingReimbursement
“Code”
“End-user”
Effectiveness
Adapted from Perfetto 2001
Different Clinical Study Hurdles
For Different Audiences

15. 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

16. 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

17. 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

18. 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

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

20. 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!!

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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)

28. 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)

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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

42. 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.

43. For more than twenty years, leading companies have depended on
THE WEINBERG GROUP when their products are at risk.
Our technical, scientific and regulatory experts deliver the crucial
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