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Incorporating Virtual Patients into Clinical Studies Adam Himes, Tarek Haddad, Medtronic Laura Thompson 1 , Telba Irony2 , Rajesh Nair1 1CDRH / FDA, 2CBER / FDA

on behalf of MDIC working group colleagues: Dawn Bardot, MDIC Anita Bestelmeyer, BD Dan Cooke, Boston Scientific Mark Horner, ANSYS Russ Klehn, St. Jude Medical Tina Morrison, OSEL / FDA Kyle Myers, OSEL / FDA Val Parvu, BD

MDIC.org

2 http://www.nejm.org/doi/full/10.1056/NEJMra1512592

“If it can be shown that these virtual patients are similar, in a precisely defined way, to real patients, future trials may be able to rely partially on virtual-patient information, thus lessening the burden of enrolling additional real patients.”

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Outline

• Motivation

• Virtual patients: physical + probabilistic modeling

• Combining virtual patients with clinical data

• Test drive: mock submission

• Review

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Motivation

Rising Demands: • Duration • Size • Data

New Factors: • Diseases • Markets • Cost Models

J. Diabetes Sci Tech (2009) 3(1) 44-55 Murbach, et.al. 2017 BMES/FDA Frontiers Lee, et.al. 2017 BMES/FDA Frontiers Dharia, et.al. 2016 BMES/FDA Frontiers

Kuntz, Insights on Global Healthcare Trends (2013)

Global demand: C

OST

15% year/year

TIME

Modeling advances:

Our ability to simulate clinical outcomes has never been better

Demand for clinical evidence has never been higher

Yet it is still challenging to incorporate prior information into new studies

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Virtual Patients as a New Source of Evidence

Bench

Animal

Human

Computer

Traditional Virtual Patient

Computer

Bench

Animal

Human

• Integrate the virtual patient in clinical study design • Use Bayesian statistical methods

• Build on 2010 FDA Guidance • Maintain clinical and statistical rigor

Future

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What Makes a Virtual Patient?

Physical Modeling

Probabilistic Modeling

Clinically Relevant

Predictions

Variability: • Age • Gender • Activity level • Implant factors • Physical tolerances

Uncertainty: • Sample size • Measurement error/bias • Model bias

Well Characterized Physics: • Structural • Electrical • Heat transfer / fluid flow Knowledge of Biology / Physiology: • Local device ↔ tissue

interactions • Failure modes • Insulin response

Clinically Relevant End Points: • ICD lead fracture • Orthopedic implant

survival • Coronary artery flow • MRI heating • Cardiac rhythm

detection • Blood glucose level

Like running a virtual clinical

study!

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Virtual Patient Example: ICD Lead Fracture

• Many applicable models, implantable defibrillator lead fracture is a good example − Straightforward − Relevant − Public domain examples

Haddad, et.al., Reliability Engineering and System Safety, 123 (2014): 145-157. Swerdlow, et.al., JACC, 67 (2016): 1358-1368.

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Virtual Patient Example: ICD Lead Fracture

• Simulate many combinations of virtual patients & clinical trial

• Propagate variability and uncertainty to predict survival with confidence bounds

Field data

Projection with 95% Confidence

Interval

in-vivo bending

patient activity

fatigue strength

INPUT OUTPUT

Haddad, et.al., Reliability Engineering and System Safety, 123 (2014): 145-157

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Bayesian Statistical Methods

• How much influence is given to the prior data?

• What if the clinical study data disagrees?

Challenges: Solution: • Method developed by MDIC

working group

• Influence of prior data determined by agreement with study data

• Maintain statistical power with fewer patients

influ

ence

disagree agree disagree agree

(ideal state)

prior data

study data

discount function

Provide a way to incorporate prior data into analysis of a clinical study

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Incorporate Virtual Patients: Step By Step

1. Compare virtual patient and current data

2. Compute strength of historical data using discount function

3. Combine virtual patient and current data

4. Statistical analysis using combined data

virtual patient data

current data

𝑝𝑝

𝑝𝑝

𝛼𝛼0

𝑛𝑛𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝑛𝑛𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑡𝑡 + 𝛼𝛼0𝑛𝑛𝑉𝑉𝑉𝑉

combined data

This part is new

Haddad, et.al, J. Biopharm Stat (2017) DOI: 10.1080/10543406.2017.1300907

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Implementation: Mock Submission • Collaboration between MDIC and FDA CDRH Division of Cardiovascular Devices • Demonstrate the engineering and statistical framework for virtual patients • MDIC sponsor team includes industry and FDA • FDA review team, just like for a real device

http://mdic.org/computer-modeling/virtual-patients/

2014 2015 2016

MDIC working group

formed

Mock submission

team identified

Mock submission

informational meeting at FDA

2nd Mock submission meeting (engineering model)

at FDA

3rd Mock submission

meeting (clinical study) at FDA

FDA commitment 2017

MDIC project initiated to

develop methods for historical data

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Mock submission details

• Hypothetical new ICD lead − Changes to insulation thickness and conductor

material, both affect fatigue life − Expected fracture rate <1% at 18 months − Single anatomical zone, single failure mode

• Clinical study design − Objectives

• Fracture rate at 18 months < 3%, type I error < 10%

− Enrollment • 200 initial patients, interim look every 30, maximum 400

patients. Up to 160 virtual patients (40%)

− Analysis • No virtual patients, fixed amount, and with a discount

function

http://mdic.org/computer-modeling/virtual-patients/

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Results

• Traditional study with no virtual patients is underpowered − 60% power / 3% type I error

• Fixed number of virtual patients

has unacceptable type I error − 96% power / 25% type I error

• Studies using virtual patients with a discount function have acceptable power AND type I error − Function #1: 80% power / 5% type I error

− Function #2: 85% power / 10% type I error

Power = % chance of success that you deserve Type I error = % chance of success you don’t deserve

lower bound of virtual patient model performance

performance requirement

Type I error suffers without discount function Power suffers

without virtual patients

http://mdic.org/computer-modeling/virtual-patients/

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MDIC Virtual Patient Statistical Framework: 2017 Communication Activities

PAST PRESENTATIONS: • Cardiovascular Research Technologies (CRT17): Leveraging Existing Information for Future

Studies: The Case for Bayesian Methods • AdvaMed Innovations Summit: Innovation in Clinical Evidence Generation, Synthesis and

Appraisal to Advance Regulatory Science for the Total Product Life Cycle • 10th Annual FDA/AdvaMed Medical Devices & Diagnostics Statistical Issues Conference:

Bayesian and Adaptive Designs UPCOMING PRESENTSTIONS • Joint Statistical Meeting (JSM): Improving the Efficiency of Medical Device Clinical Trials by

Combining Simulations and Experiments. Baltimore, 8/01/17, 2:00 PM - 3:50 PM ONLINE MDICx SERIES EVENTS: mdic.org/MDICx

• Leveraging Existing Information for Future Studies: The Case for Bayesian Methods: Encore

presentation from CRT17 with Case Study and Bayesian/Adaptive Regulatory negotiation updates. • Virtual Patient R Code Access and Use: Public release of the Virtual Patient R Code (on CRAN)

with demonstrations applying the model to various study types

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Summary

• Virtual patients can improve the clinical decision process while exposing fewer patients to clinical trials

• Discount function controls the influence of virtual patients

• The statistical methods are ready – we just need the right applications!

• Without collaboration between FDA and industry, we would not be here today!