Contains Nonbinding Recommendations 
 
 
 
Leveraging Existing Clinical Data 
for Extrapolation to Pediatric Uses 
of Medical Devices  
 
 
Guidance for Industry and Food 
and Drug Administration Staff 
 
 
Document issued on June 21, 2016.  
This document will be in effect as of September 19, 2016. 
 
The draft of this document was issued on May 6, 2015. 
 
For questions regarding this document, contact Jacqueline Francis (CDRH) at (301) 796-
6405 (Jacqueline.Francis@fda.hhs.gov), CDRHPediatricExtrapolation@fda.hhs.gov,  or 
the Office of Communication, Outreach, and Development (CBER) at 800-835-4709 or 
240-402-8010.  
 
 
 
 
U.S. Department of Health and Human 
Services 
Food and Drug Administration 
Center for Devices and Radiological Health 
      
 
Center for Biologics Evaluation and Research 
 
 
 
 
 
 
 
 
1 
 
  Contains Nonbinding Recommendations 
 
 
 
 
 
 
Preface 
 
 
Public Comment 
 
You may submit electronic comments and suggestions at any time for Agency 
consideration to http://www.regulations.gov .  Submit written comments to the Division of 
Dockets Management, Food and Drug Administration, 5630 Fishers Lane, Room 1061, 
(HFA-305), Rockville, MD 20852.  Identify all comments with the docket number [FDA-
2015-D-1376].  Comments may not be acted upon by the Agency until the document is 
next revised or updated. 
 
Additional Copies 
 
CDRH 
 
Additional copies are available from the Internet. You may also send an e-mail request to 
CDRH-Guidance@fda.hhs.gov to receive a copy of the guidance.  Please use the 
document number (1827) to identify the guidance you are requesting. 
 
CBER 
 
Additional copies are available from the Center for Biologics Evaluation and Research 
(CBER) by written request, Office of Communication, Outreach, and Development 
(OCOD), 10903 New Hampshire Ave., WO71, Room 3128, Silver Spring, MD 20903, or 
by calling 1-800-835-4709 or 240-402-8010, by email, ocod@fda.hhs.gov, or from the 
Internet at  
http://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInf
ormation/Guidances/default.htm.  
 
 
 
 
 
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  Contains Nonbinding Recommendations 
 
Table of Contents 
 
1. Introduction ..................................................................................................................... 4 
2. Overview ......................................................................................................................... 5 
3. Background ..................................................................................................................... 7 
4. Why Extrapolate Adult Data for Pediatric Use? ........................................................... 10 
5. Borrowing Strength from Adult Data ........................................................................... 11 
5.1 Full and Partial Extrapolation ................................................................................. 12 
5.2 Extrapolation for Effectiveness vs. Safety .............................................................. 13 
6. Pediatric Extrapolation Decision Process.....................................................14 
6.1 Pediatric Extrapolation Decision Tree .................................................................... 14 
6.2 Questions in the Pediatric Extrapolation Decision Tree ......................................... 17 
7. Factors that Could Limit Extrapolation ........................................................................ 21 
8. Uncertainty in Extrapolating Data ................................................................................ 22 
9. Statistical Methodology for Extrapolation .................................................................... 23 
9.1 The Bayesian Hierarchical Model and Exchangeability of Studies........................ 24 
9.2 Age-Related Covariates Associated With Device Outcomes ................................. 25 
Appendix A. Examples of the Decision Process for Extrapolation .................................. 27 
A.1 A Hypothetical Example of Full Extrapolation ..................................................... 27 
A.2 A Hypothetical Example of Partial Extrapolation with Relevant Age-Associated 
Differences between Populations Accounted for Via Modeling .................................. 28 
A.3 A Hyopthetical Example of Partial Extrapolation ................................................. 29 
A.4 Hypothetical Examples where Extrapolation is not Recommended ...................... 29 
A.4.1 Hypothetical Example where Extrapolation is not Recommended because of Quality 
of Data ................................................................................................................................... 29 
A.4.2 Hypothetical Example where Extrapolation is not Recommended because of Relevant 
Differences ............................................................................................................................ 30 
A.5 An Example of an Actual Extrapolation ................................................................ 30 
Appendix B: Details on Statistical Modeling for Extrapolation ....................................... 33 
B.1 A Three-Level Hierarchical Model ........................................................................ 33 
B.3 Age-Related Covariates Associated With the Device Effect or Outcome ............. 34 
B.4. Extrapolation From a Single Adult Study ............................................................. 35 
B.5 Additional Methods for Extrapolation ................................................................... 35 
References ......................................................................................................................... 37 
 
Figures and Tables 
Figure 1. Pediatric Extrapolation Decision Tree ............................................................... 16 
Figure 2. Three-Level Hierarchical Model Structure Example: Studies Within Patient 
Populations Have Different But Related Effects .............................................................. 34 
 
 
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  Contains Nonbinding Recommendations 
 
Leveraging Existing Clinical Data 
for Extrapolation to Pediatric Uses 
of Medical Devices 
 
 
Guidance for Industry and Food 
and Drug Administration Staff  
 
 
This guidance represents the current thinking of the Food and Drug Administration 
(FDA or Agency) on this topic.  It does not establish any rights for any person and is 
not binding on FDA or the public.  You can use an alternative approach if it satisfies 
the requirements of the applicable statutes and regulations.  To discuss an alternative 
approach, contact the FDA staff or Office responsible for this guidance as listed on the 
title page.   
1. Introduction  
 
The Food and Drug Administration (FDA) aims to promote safe and effective device use 
in pediatric patients, while ensuring device approvals are based on valid scientific 
evidence.1  Currently, there is a paucity of scientific evidence available to substantiate 
submissions for devices that are indicated for use in the diagnosis or treatment of 
pediatric patients.  Leveraging relevant available clinical data, when appropriate, may 
lead to more devices being granted marketing authorization for pediatric indications, 
which will increase the availability of medical devices with appropriate labeling to 
support safe and effective device use in pediatric patients.  This approach will potentially 
streamline the process for establishing a pediatric intended use claim, and enhance and 
encourage pediatric device development programs. 
   
                                                 
1 Valid scientific evidence is 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 
fairly and responsibly be concluded by qualified experts that there is reasonable assurance of the safety and 
effectiveness of a device under its conditions of use. (21 CFR 860.7(c)(2)) 
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  Contains Nonbinding Recommendations 
 
This guidance does not change the regulatory threshold for valid scientific evidence.  
Instead, the document seeks to provide clarity and predictability for device sponsors and 
to ensure consistency within FDA regarding the specific criteria that should be 
considered when deciding whether leveraging existing clinical data to support pediatric 
claims is appropriate, and if so, to what extent.  When considering extrapolation, 
sponsors are encouraged to engage FDA early in product development planning. 
 
FDAs guidance documents, including this guidance, do not establish legally enforceable 
responsibilities. Instead, guidance documents describe the Agencys current thinking on a 
topic and should be viewed only as recommendations, unless specific regulatory or 
statutory requirements are cited. The use of the word should in Agency guidance 
documents means that something is suggested or recommended, but not required.  
2. Overview  
 
The objectives of this guidance are: (1) to increase the availability of safe and effective 
pediatric devices by providing a roadmap for leveraging relevant existing clinical data for 
use in demonstrating a reasonable assurance of safety and effectiveness in pre-market 
approval applications (PMAs) and de novo requests2, as well as for use in supporting 
approvals of humanitarian device exemptions (HDEs)3; (2) to explain the circumstances 
in which it may be appropriate to leverage existing clinical data to support pediatric 
device indications and labeling; (3) to outline the approach FDA uses to determine 
whether extrapolation is appropriate, and, to what extent the data can be leveraged; and 
(4) to describe statistical methodology that can be used to leverage the data in a way that 
increases precision for pediatric inferences. 
 
For the purposes of this document, "extrapolation" refers to the leveraging process 
whereby an indication for use of a device in a new pediatric patient population can be 
supported by existing clinical data from a studied patient population.  That is, when 
existing data are relevant to a pediatric indication and determined to be valid scientific 
evidence, it may be scientifically appropriate to attempt to extrapolate such data to a 
pediatric use in support of demonstrating a reasonable assurance of effectiveness or 
probable benefit and, occasionally, safety.  
 
                                                 
2 A request for evaluation of automatic Class III designation, also known as a de novo request, described 
under Section 513(f)(2) of the Food Drug and Cosmetic Act, is intended to provide a pathway to Class I or 
Class II classification for medical devices for which general controls or general and special controls 
provide a reasonable assurance of safety and effectiveness, but for which there is no legally marketed 
predicate device.   
3 In accordance with Section 520(m)(2) of the Food Drug and Cosmetic Act, HDE approval is based upon, 
among other criteria, a determination by FDA that the device will not expose patients to an unreasonable or 
significant risk of illness or injury and the probable benefit to health from use of the device outweighs the 
risk of injury or illness from its use while taking into account the probable risks and benefits of currently 
available devices or alternative forms of treatment. 
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  Contains Nonbinding Recommendations 
 
This guidance explains when and how existing clinical data in another studied population 
(such as adults, or a different pediatric subpopulation) may be leveraged (extrapolated) 
to support marketing approval and labeling of medical devices for use in pediatric 
patients.  In order to make decisions about the effectiveness and safety of a medical 
device in pediatric patients, FDA considers the totality of the evidence available.  As with 
any PMA, HDE or de novo request, FDA will still consider clinical data (whether 
extrapolated or not) alongside other forms of scientific evidence from assessments of 
device performance (e.g., preclinical testing, engineering models, biocompatibility, 
virtual patient simulations, statistical models) to determine whether the sponsor has 
demonstrated a reasonable assurance of safety and effectiveness (or probable benefit, for 
HDEs).   
 
This guidance should be used in conjunction with other guidance documents for pediatric 
medical devices and other applicable device-specific guidance documents to help ensure 
that medical devices intended for use in the pediatric population provide reasonable 
assurance of safety and effectiveness (or probable benefit, for HDEs). 
 
The scope of this guidance includes medical devices subject to the PMA, HDE, or de 
novo premarket requirements where a pediatric indication is sought.  For these premarket 
submissions, it may be appropriate to extrapolate existing clinical data when the course of 
the disease or condition and effects of the device are sufficiently similar in adults and 
pediatric patients, and the existing data are determined to be valid scientific evidence.  
Extrapolation should be limited to circumstances in which endpoints used in the adult 
data sources are relevant to the pediatric population, and the quality of these data is high.  
In this context, it is important to note that the consideration of whether to borrow existing 
data to extrapolate for the demonstration of effectiveness for a pediatric population is 
independent from the consideration of whether to extrapolate for the assurance of safety.  
In other words, the criteria that govern the decision of whether or not to extrapolate are 
considered separately for effectiveness and for safety.   
 
The policies described in this guidance are not applicable to premarket notification 
submissions (510(k)s) in which a pediatric indication is proposed for a medical device.  
The standard for clearance for a device submitted in a 510(k) is substantial equivalence to 
a legally marketed (predicate) device.  This is different than the approval standard 
referenced by the Pediatric Medical Device Safety and Improvement Act (PMDSIA).4  
Because PMDSIA does not address the use of extrapolated data to demonstrate 
substantial equivalence, and because the policies in this guidance are specifically tailored 
to show how extrapolated data may be used to demonstrate a reasonable assurance of 
effectiveness, safety and/or probable benefit rather than substantial equivalence, this 
guidance is presently intended only for medical devices subject to the PMA, de novo and 
HDE premarket requirements where a pediatric indication is sought.  Future guidance 
                                                 
4 Title III of Food and Drug Administration Amendments Act is the Pediatric Medical Device Safety and 
Improvement Act (PMDSIA) of 2007. 
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  Contains Nonbinding Recommendations 
 
documents may provide more details to comprehensively address the issues related to the 
510(k) regulatory pathway. 
 
This guidance facilitates efforts to address an unmet medical device need for pediatric 
patients.  The framework described herein is one tool to make optimal use of what is 
already known about device effects in other populations to support indications in the 
pediatric population. 
3. Background 
 
When considering extrapolation of existing data for pediatric device indications, it is 
important to understand how pediatric subpopulations are defined in the statutory 
provisions governing the regulation of medical devices.  Section 520(m)(6)(E)(i) of the 
Federal Food, Drug, and Cosmetic Act (FD&C Act)5 defines pediatric patients as 
persons aged 21 or younger at the time of their diagnosis or treatment (i.e., from birth 
through the 21st year of life, up to but not including the 22nd birthday). Pediatric 
subpopulations are defined in Section 520(m)(6)(E)(ii) (and adopted by reference in 
Section 515A(c) of the FD&C Act) to be neonates, infants, children, and adolescents.  
 
Age ranges for these pediatric subpopulations are as follows:  
 
  Neonates: from birth through the first 28 days of life  
  Infants: 29 days to less than 2 years 
  Children: 2 years to less than 12 years  
  Adolescents: aged 12 through 21 (up to but not including the 22nd birthday) 
 
Despite these definitions, extrapolation may not necessarily follow directly from them.  
For example, the course of an orthopedic disease may be determined by factors that are 
not categorized into the subpopulations listed above, but instead are categorized by 
skeletal maturity.  However, while one biological factor may make consideration of 
extrapolation feasible, there may be other unique pediatric biological or developmental 
factors that are relevant to the safety or effectiveness of the device that should also be 
considered. All relevant biological characteristics should be considered. 
 
For certain diseases, older adolescents are sufficiently similar to adults aged 22 and over 
such that extrapolation would only be needed for younger adolescents and children.  In 
general, if a device is approved for the adult population (aged 22 and over), it may be 
easier to extrapolate to an adolescent sub-population than to sub-populations of a younger 
age. 
 
                                                 
5 Available at 
http://www.fda.gov/regulatoryinformation/legislation/federalfooddrugandcosmeticactfdcact/default.htm 
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  Contains Nonbinding Recommendations 
 
In 2004, FDA published a guidance document entitled Premarket Assessment of 
Pediatric Medical Devices6 in an attempt to clarify the types of information needed to 
provide reasonable assurance of safety and effectiveness of medical devices intended for 
use in pediatric patients and to promote the development of these devices.  That 
document indicates that data can be extrapolated to support effectiveness and, on a 
limited basis, safety for premarket approval applications (PMAs) when consistent with 
scientific principles. That guidance states the following:  
 
If it is determined that clinical data are needed, it may be that the course of the 
disease and the devices effects are similar in adult and pediatric patients. In such 
a situation, the pediatric indication may be supported by the adult data with 
limited additional safety data in the pediatric population.7 
 
That guidance document was updated in 2014 to make clear that, as with other forms of 
valid scientific evidence used to demonstrate effectiveness and safety for a device 
intended for a pediatric population, the amount and type of extrapolated data necessary to 
support a pediatric indication for a device varies: 
 
As is true for medical devices in general, FDA does not believe that clinical data 
will be necessary to demonstrate effectiveness and safety for all devices intended 
for pediatric populations. The agency recognizes that the amount and type of 
evidence required will depend on a number of factors, including the nature of the 
device, what is already known about the product in the adult population (if 
relevant), what is known or can be extrapolated about the device to the pediatric 
population, and the underlying disease or condition being treated. In some cases, 
well-designed bench and animal testing will be sufficient to evaluate the device. 
In others, clinical data may be needed to evaluate the safety and effectiveness of 
the device.4 
 
Congress was aware of the 2004 version of this guidance document when it passed the 
Food and Drug Administration Amendments Act of 2007 (FDAAA). The House Report 
(H.R. Rep. 110-225) states: 
 
FDA addressed premarket review of medical devices intended for pediatric 
patients by issuing guidance in May 2004 entitled Premarket Assessment of 
Pediatric Medical Devices. The guidance was published pursuant to the Medical 
Device User Fee and Modernization Act, which contained several provisions 
intended to promote the development of safe and effective pediatric devices. In 
this guidance, FDA defined the age ranges for pediatric subpopulations, 
identified the types of information needed to provide reasonable assurance of 
                                                 
6 Available at  
http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm089740.htm 
 
7 Available at  
http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm089740.htm 
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  Contains Nonbinding Recommendations 
 
the safety and effectiveness of medical devices intended for use in the 
pediatric population, and described the protections that sponsors should consider 
for pediatric subjects involved in clinical trials (emphasis added). 
 
Title III of FDAAA is the Pediatric Medical Device Safety and Improvement Act 
(PMDSIA)8 of 2007. PMDSIA specifically authorized the use of adult data to 
demonstrate pediatric effectiveness9, stating:  
   
If the course of the disease or condition and the effects of the device are 
sufficiently similar in adults and pediatric patients, the Secretary may conclude 
that adult data may be used to support a determination of a reasonable assurance 
of effectiveness in pediatric populations, as appropriate.  
 
In addition to allowing for the extrapolation of adult data to pediatric populations, the 
provision indicates that, when appropriate, data can be extrapolated from one pediatric 
subpopulation to another. 
 
While PMDSIA addresses the extrapolation of existing data to support a determination of 
a reasonable assurance of effectiveness, it does not address safety data.  However, there 
may be specific cases where it will be appropriate to consider extrapolation of existing 
clinical safety data to support or enhance evidence for pediatric indications for medical 
devices, including the cases discussed in this guidance (e.g., the effects of the device 
under consideration are identical when used in pediatric and adult populations and the 
course of the disease or condition and associated risk factors are the same between the 
two populations).   
 
Given the potential for similarity in disease or condition, device attributes and treatment 
effects between patient populations, and the availability of other nonclinical forms of 
evidence to assess safe device performance, extrapolating for safety in medical devices in 
specific circumstances could be appropriate and consistent with the requirement to base 
approval decisions on valid scientific evidence.  Because the mechanism of action for 
devices is often well-characterized and often fairly localized, non-clinical forms of 
scientific evidence may provide information about device performance characteristics 
related to safe device functioning (e.g., preclinical testing, engineering models, computer 
modeling, or other nonclinical data).  The potential availability of these types of data for 
medical devices provides further support for the use of extrapolated clinical data to 
demonstrate safety in pediatric patients.  However, full extrapolation10 of safety data is 
                                                 
8 Available at 
http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/DevelopmentResources/UCM049870
.pdf 
9 The term effectiveness is defined as follows: There is reasonable assurance that a device is effective 
when it can be determined, based upon valid scientific evidence, that in a significant portion of the target 
population, the use of the device for its intended uses and conditions of use, when accompanied by 
adequate directions for use and warnings against unsafe use, will provide clinically significant results (21 
CFR 860.7). 
10 See definition of full extrapolation in Section 5.1. 
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  Contains Nonbinding Recommendations 
 
expected to occur rarely. The appropriateness of extrapolation for effectiveness and/or 
safety is considered independently on a case-by-case basis by following the decision tree 
described in Section 6.  
 
The specific threshold for approval addressed under PMDSIA for the use of extrapolated 
data is reasonable assurance of effectiveness.  Because both PMA approval and 
granting a de novo request require a reasonable assurance of safety and effectiveness, 
both PMAs and de novo requests are covered within the scope of PMDSIA and thus 
medical devices submitted in these application types are addressed in the context of this 
guidance. 
 
PMDSIA does not specifically address the use of extrapolated data to support a 
determination of probable benefit in pediatric populations, as necessary in HDEs.  
However, the policies in this guidance document are sufficiently detailed to address the 
use of extrapolated data to demonstrate probable benefit for medical devices subject to an 
HDE.  The use of extrapolated data in these circumstances may be particularly useful 
given the rarity of the diseases and/or conditions that are addressed by medical devices 
that are submitted in HDE applications. 
 
This guidance does not change the threshold for regulatory approval or the meaning of 
valid scientific evidence.  When existing clinical data are relevant and appropriate for 
leveraging, the amount of prospective clinical data in the pediatric population needed to 
demonstrate a reasonable assurance of effectiveness and/or safety (or that probable 
benefits outweigh risks, for HDEs) may be reduced.  If the existing clinical data are not 
appropriate for leveraging, or if they are insufficient to meet the threshold of valid 
scientific evidence, data will not be extrapolated.    
4. Why Extrapolate from Adult Data for Pediatric Use? 
 
The extrapolation of adult data for pediatric use may benefit pediatric patients by 
increasing the availability of medical devices with appropriate labeling to support safe 
and effective pediatric use.  Extrapolation, when appropriate, facilitates the use of 
available relevant data by making use of existing clinical data that may be helpful for 
understanding device performance in pediatrics. This is similar to the Bayesian concept 
of borrowing from one population or data set (e.g., prior adult information) to come to a 
posterior conclusion about another population (e.g., pediatric effectiveness or safety)11.  
Extrapolation of adult data is limited to situations in which the course of the disease or 
condition and the effects of the device are sufficiently similar in adults and pediatric 
patients.  For example, data from studies of devices that create intracranial arteriotomies 
in adults may offer insights into their effectiveness in pediatric patients between the ages 
                                                 
11 See FDAs Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials, available at 
http://www.fda.gov/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm071072.htm 
10 
 
  Contains Nonbinding Recommendations 
 
of 13 and 21 because it is widely accepted that cerebral vasculature of this age group is 
similar to that of adults.  
 
There are many potential challenges involved in conducting pediatric clinical trials to 
support pediatric indications for devices. For example: 
 
  Small and diffusely scattered potential pediatric populations lead to small (trial 
sample sizes. 
  Challenges exist in enrollment and consent procedures, which could increase the 
length of time needed to conduct clinical trials. 
  There are more variations in pathophysiology, physiology, anatomy, and human 
factors in children and within pediatric subpopulations as compared to adults. 
  Reference samples to test for or determine surrogate outcome measures may 
require an amount of blood too voluminous to obtain safely from a neonate or 
small child. 
 
At least in part because of these challenges, relatively few devices have pediatric-specific 
indications and labeling. Yet off-label use of adult devices, without labeling information 
to guide safe and effective use in pediatric patients, is not uncommon.  The use of 
existing clinical data when appropriate may reduce the need to prospectively conduct 
large pediatric clinical trials by bolstering other scientific evidence supporting a 
reasonable assurance of safety and effectiveness in a pediatric population. Extrapolation 
may encourage industry to provide performance data (e.g., bench data and supplemental 
clinical data from pediatric patients) to support a pediatric indication, which may reduce 
unsafe off-label use by promoting proper labeling for use in pediatric patients even when 
limited pediatric data are available.  Informative labeling of a device which promotes safe 
and effective pediatric use ultimately benefits patients. 
5. Borrowing Strength from Adult Data 
 
Extrapolation enables a sponsor to leverage adult data to support demonstration of a 
reasonable assurance of effectiveness and possibly the safety of a medical device for 
pediatric use. The quantitative information provided by existing adult data can be 
incorporated in one of two ways.  The adult data can stand in as a substitute for pediatric 
data. Alternatively, the adult data can be used to supplement pediatric data within a 
statistical model. This statistical model, which combines the two data sources, potentially 
bolsters the valid scientific evidence available to demonstrate effectiveness and/or safety 
in the pediatric (sub)population.  This type of combination of data sources is known as 
borrowing strength in statistical literature (Carlin & Louis, 2009)12. Such borrowing 
                                                 
12 Carlin, B., & Louis, T. (2009). Bayesian methods for data analysis. Boca Raton, FL: CRC Press. 
 
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  Contains Nonbinding Recommendations 
 
can bolster the sample size of a prospective pediatric study.  The exact model used to 
borrow strength may vary case by case. However, for all models, the extent of leveraging 
depends, in part, on the similarity between borrowed data and any pediatric data that will 
be collected. 
 
The extent of borrowing may also be moderated by clinical judgments that are not 
inherently implied by the statistical modeling.  This may include consideration of the 
quality of the data, the particulars of the populations and the studies, and whether such 
data are intended to demonstrate either safety or effectiveness (or both).  Effectiveness 
and safety often have different endpoint assessments in a study.  In addition, the study 
design could be different for different endpoints, or there could be different 
considerations in the pediatric population for safety versus effectiveness. Therefore, 
safety is considered independently from effectiveness in deciding whether or not 
extrapolation may be appropriate.  Section 6 provides more details about important 
information needed in the decision to extrapolate. 
 
Existing clinical data from adults and some non-clinical studies may provide information 
about device safety which is relevant to risks in children.  For some devices, the 
mechanism of action is expected to be similar in adults and pediatric patients.  In these 
cases, non-clinical forms of scientific evidence may provide some information about 
many device performance characteristics related to safe device functioning (e.g., 
preclinical testing, engineering models, computer modeling, or other nonclinical data).  
However, the sole use of non-clinical data as the basis for valid scientific evidence 
regarding safety is expected to be exceedingly rare. Likewise, existing clinical data from 
adults may provide information about device safety which is relevant to risks in children.  
Based on the nature of the similarities and differences between target populations and on 
the quality of the existing data, additional clinical studies in pediatric patients may be 
warranted to supplement the existing data to provide valid scientific evidence about 
device safety. 
   
Types of existing data sources that may be considered for extrapolation include (but are 
not restricted to) data from a variety of clinical investigations (e.g., randomized 
controlled trials, single arm studies, and from any individual treatment arm), historical 
clinical data, reference samples, and published literature.  
5.1  Full and Partial Extrapolation 
 
Existing clinical data may be leveraged, either fully or partially, via statistical modeling 
to support a reasonable assurance of safety or of effectiveness in a pediatric patient 
population.  These two types of extrapolation are defined as follows:  
 
  Full Extrapolation: Existing clinical data are used directly (i.e., as a complete 
substitute) for prospective pediatric clinical data in support of a determination of a 
reasonable assurance of effectiveness or of safety for a pediatric device.  No 
prospective pediatric clinical data are anticipated for the endpoint being fully 
extrapolated. However, as with any PMA, de novo or HDE, FDA will consider 
12 
 
  Contains Nonbinding Recommendations 
 
this alongside other data sources, such as virtual patient simulations, bench data, 
mechanical models, literature studies or case reports, as further valid scientific 
evidence supporting a reasonable assurance of safety and effectiveness in the 
intended pediatric sub-population.  Given the range of potential differences 
between adult and pediatric patients, full extrapolation of existing clinical data to 
demonstrate safety is expected to be rare. 
  Partial Extrapolation: Existing data are combined via a statistical model with 
pediatric data sources or prospective pediatric clinical data in support of 
demonstrating a reasonable assurance of effectiveness or of safety for a pediatric 
device.  The construction of such a statistical model is anticipated to require the 
availability of measured variables that will help connect the adult outcomes to the 
pediatric outcomes.  If necessary variables are not available in the data sources, 
partial extrapolation may not be appropriate. If the model is determined to be 
appropriate, then the inferences obtained from it may be used to support a 
pediatric indication. 
 
Full extrapolation requires a significant amount of trust in the relevance and quality of the 
adult data because they will constitute the sole clinical data to support effectiveness and 
possibly safety of the device in pediatric patients.  Partial extrapolation also requires trust 
in the adult data, specifically, the trust that the adult data are similar to what is expected 
to occur in pediatric patients. Furthermore, because the actual extent of partial 
extrapolation (or borrowing) will be determined after the pediatric data are gathered, 
there is some verification of whether extrapolation is ultimately appropriate. If 
extrapolation is ultimately not appropriate, then the pediatric data will need to be 
sufficient alone to support marketing approval.  Section 6 of this document describes the 
approach that is used to determine whether existing clinical data sources are candidates 
for borrowing either fully or partially to extrapolate either effectiveness, safety, or both to 
a pediatric population. 
5.2   Extrapolation for Effectiveness vs. Safety 
 
FDA believes that existing clinical data can be extrapolated when appropriate to support 
either effectiveness or safety or both in medical devices.  However, since the endpoints 
related to effectiveness are likely different from those for safety in a given study, and 
because the quality of data may differ in some circumstances, the decisions of whether to 
extrapolate existing data for safety or effectiveness (or both) are made independently.  
For example, in medical devices, there may be circumstances where FDA may conclude 
(based on the flowchart in Section 6.1) that full extrapolation of adult data is appropriate 
for effectiveness, but there is still a need for a safety study in a pediatric population.   
 
Because of the physiological differences between adult and pediatric patients that may 
affect device safety and the inherent difficulties in designing and powering clinical 
studies that provide comprehensive assessments of safety, extrapolation for safety is 
expected to be rarer than extrapolation for effectiveness. In fact, it may often be the case 
that if extrapolation for effectiveness is not warranted, then extrapolation for safety will 
13 
 
  Contains Nonbinding Recommendations 
 
likely also not be warranted.  However, we believe that there are cases where 
extrapolation for safety might be appropriate in some cases to support a pediatric 
indication.  Again, extrapolated data will be considered with the totality of evidence to 
either support or not support a reasonable assurance of safety and effectiveness (or 
probable benefit in HDEs). 
6. Pediatric Extrapolation Decision Process 
 
The extrapolation approach described in this guidance document provides a framework 
for considering whether or not the extrapolation of existing clinical data is appropriate to 
support a pediatric indication, and if so, to what extent. 
 
The appropriateness of extrapolation largely depends on three main factors: (1) the 
similarity of the existing adult response data and/or population characteristics to the 
intended pediatric sub-population; (2) the quality of the adult data in terms of study 
design, data collection, and measurement; and (3) whether extrapolated data may be used 
to fairly and responsibly decide whether there is a reasonable assurance of the safety and 
effectiveness (or probable benefit, for HDEs) of a medical device (i.e., constitute valid 
scientific evidence).  Broadly, factors that can affect data quality include study design, 
data collection and measurement, and the applicability of these data with consideration of 
the current standard of practice for the disease or condition being treated.    
  
When both similarity and quality are determined to be sufficiently high, there is a greater 
level of certainty that the existing data can be appropriately considered for extrapolation 
to the intended pediatric subpopulation. If neither similarity nor data quality are high, 
then the existing adult data may be inappropriate to use for extrapolation purposes.   
 
6.1 Pediatric Extrapolation Decision Tree 
 
The following decision tree (see Figure 1 below) can be used by sponsors and FDA 
review staff as a tool to help determine whether extrapolation of existing clinical data 
might be appropriate and, if so, whether extrapolation should be full or partial.   
 
Please note that the approach described in the decision tree is intended as an aid to decide 
whether or not extrapolation can be considered in a specific situation.  A conclusion from 
the decision tree that extrapolated data may be used does not necessarily mean that these 
data will support an approval decision for the PMA, de novo, or HDE application.  If it is 
determined that existing data can be extrapolated in some manner to support a pediatric 
indication, the extrapolated data would be considered in conjunction with the totality of 
evidence that will either support or not support a reasonable assurance of safety and 
effectiveness (or probable benefit, for HDEs).   
 
The general approach of the decision tree is to first consider whether the treated condition 
occurs at all in the intended pediatric sub-population, and, if so, then, whether available 
14 
 
  Contains Nonbinding Recommendations 
 
adult or other extrapolated data related to that condition and/or the effect of the device are 
relevant to the intended pediatric sub-population. One potential (and perhaps readily 
available) source of relevant data is prior clinical studies of the device for the adult 
indication.  If these adult studies use an endpoint that is similar to the primary endpoint of 
interest in the pediatric population, then the studies may be relevant for extrapolation.  If 
no relevant data are available from any prior adult studies, then extrapolation should not 
be used.   
Second, consider to what extent the adult data are similar to what may be seen in the 
pediatric population.  For example, are there expected differences in the device 
characteristics, patient characteristics, or disease characteristics between the identified 
adult population and the intended pediatric (sub)population(s)?  If there are expected 
differences, extrapolation might not be appropriate. The differences could contribute to a 
high level of uncertainty regarding the expected device effect such that the adult data 
cannot support a pediatric indication. On the other hand, if such differences are minimal 
and can be explained by covariates or surrogate variables in the data, partial extrapolation 
may be appropriate (See Section 9.2). If there are no expected differences, then full 
extrapolation could be an option if the quality of the adult data is such that substituting 
adult data for pediatric data is considered appropriate. 
 
The decision to extrapolate for safety and the decision to extrapolate for effectiveness are 
made by going through the decision tree for each of these factors separately.  In the tree, 
there will be items that will remain constant for either decision. For example, when 
considering whether to extrapolate for safety, effectiveness or both, the considerations 
related to the similarities or differences in disease progression and device characteristics 
between the adult and pediatric populations may be the same. However, endpoints and 
the quality of data relating to these endpoints may differ when considering the safety or 
effectiveness components of a prior study.   
 
15 
 
  Contains Nonbinding Recommendations 
 
Figure 1. Pediatric Extrapolation Decision Tree 
     
 
* Note that if all five questions in Box C are answered no, the direction from C is no. If at least one of 
the five is answered yes, the direction from C is yes. 
**The agency relies upon only valid scientific evidence to determine whether there is reasonable assurance 
that a device is safe and effective.  Valid scientific evidence is 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 fairly and responsibly be concluded by qualified experts that there is reasonable assurance 
of the safety and effectiveness of a device under its conditions of use.  21 CFR 860.7(c)(1)&(2). 
 
The questions in the Pediatric Extrapolation Decision Tree are a guide for what to 
consider when determining the appropriateness of extrapolation of adult data for pediatric 
indications. These questions are designed to promote discussion between FDA review 
staff and sponsors while facilitating consistency among FDA review staff. Considerations 
of extrapolation of any type should be discussed with FDA staff throughout the device 
protocol planning stages. It is highly recommended that the pre-submission pathway be 
16 
 
  Contains Nonbinding Recommendations 
 
used to explore such options.13 A post-market surveillance study may be required, 
particularly in situations where full extrapolation of safety data is agreed upon by FDA 
staff and device manufacturers.14 
6.2  Questions in the Pediatric Extrapolation Decision Tree 
 
This section provides more detail about using the questions in the Pediatric Extrapolation 
Decision Tree to make a decision regarding the appropriateness of extrapolation. The first 
two questions are asked to determine whether extrapolation should be considered at all.  
Within the tree, these are referred to as Relevancy Questions because they pertain to 
whether adult data are relevant for extrapolation. 
 
Question A: Does the treated disease or condition in question occur in pediatric 
(sub)populations? 
 
If the answer is no, extrapolation of adult data is not appropriate. If the answer is yes, 
proceed to question B. 
 
Question B: Is there an endpoint present in the existing data source that measures device 
effects relevant to the intended pediatric (sub)population(s)? 
 
In order to borrow confidently from adult data there should be either: (1) the same 
variable measured in the adult data as would be expected to be measured as the primary 
endpoint in the intended pediatric population, or (2) a variable measured in the existing 
adult data that is sufficiently related to the primary endpoint expected to be measured in 
the pediatric population. For the latter case, a reliable and valid model might be used to 
predict the endpoint for the pediatric population using the endpoint from the adult 
population. Reliability and validity of the model should be established from prior 
investigations. One possibility is to use a validated surrogate endpoint in the adult data 
set(s) that has been shown to predict a different (perhaps longer-term) endpoint of 
interest. For example, a device that is used to treat diabetes may rely on validated adult 
and pediatric surrogate endpoints such as serum glucose levels or HbA1c to measure 
actual device outcomes. 
 
If the answers to questions A and B are yes, continue along the decision tree. The next 
five questions are addressed as a set (Questions C.). Within the tree, we label these 
questions as pertaining to Similarity. The questions in Box C ask whether there are 
differences between the adult and pediatric populations, or differences between the 
device characteristics for each population, that could impact the safety and effectiveness 
                                                 
13http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UCM31117
6.pdf  
 
14 http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMA/pss.cfm  
 
17 
 
  Contains Nonbinding Recommendations 
 
of the proposed device in pediatric patients.  The questions in Box C serve to address 
whether or not the course of the disease and the effects of the device are sufficiently 
similar in adults and pediatric patients.   
 
To determine whether the effectiveness or safety of the device is sufficiently similar 
between adult and pediatric populations (or pediatric sub-populations), the direction and 
magnitude of the device effect should be considered.  The direction of the device effect 
on the outcome should be the same across populations.  By direction of the device effect, 
we mean that if the device has a benefit for adults, then it should also have a benefit for 
the intended pediatric population for the endpoint under study.  With respect to 
magnitude, the benefit should be similar between populations (not necessarily the same).  
Evaluating the extent of similarity of the magnitude between populations should be 
considered on a case-by-case basis.   
 
In many cases, devices that are intended to benefit an adult population also benefit 
pediatrics. For example, a suture intended to close wounds in adults may have a similar 
effect on a pediatric wound.  Therefore, the direction of device effect is the same and 
magnitude of the effect is similar.  Alternatively, some devices that are intended to 
benefit an adult population do not benefit a pediatric population, and might even worsen 
the pediatric patients condition.  For example, a device used for damaged joints in adults 
might be considered for the same indication in children; however, because children do 
not have closed growth plates, the device could cause significant problems for children 
who are still actively growing.  Therefore, the direction of the device effect is not the 
same and the magnitude of effect is not a factor in the consideration of extrapolation.   
 
Differences tend to increase the amount of uncertainty in statistical inference when 
extrapolating from adult to pediatric patients.  If all of the five questions are answered 
no for either safety or effectiveness or both, then full extrapolation can be considered if 
the adult data are of sufficiently high quality. If any of the questions in Box C are 
answered yes, then the review team should determine whether the adult data provide 
useful information for partial extrapolation by revisiting answers to the questions within 
Box C as well as any additional important information.  
 
Questions Box C. 
 
Question C-1: Is the device implanted or in contact with the body, and, if so, does either 
the location or duration of implantation differ between the adult and intended pediatric 
(sub)population(s) in such a way that the safety or effectiveness of the device could be 
impacted in a clinically meaningful way?  
 
If the location or duration of implantation differs and the difference is expected to impact 
device safety or effectiveness, then full extrapolation is probably not feasible.  However, 
partial extrapolation may still be viable if the quality of adult data is sufficiently high 
such that statistical and clinical modeling can account for the difference, and FDA can 
fairly and responsibly use such data to conclude that there is reasonable assurance of 
effectiveness and/or safety of the device. 
18 
 
  Contains Nonbinding Recommendations 
 
 
Question C-2: Are there differences in device characteristics between pediatric and adult 
use that could impact either device safety or effectiveness in the pediatric 
(sub)population(s) in a clinically meaningful way? 
 
For instance, sometimes device modifications (e.g., design, materials, and mechanisms of 
use) must be made in order to use a device in a pediatric population. To the extent these 
modifications could impact device safety or effectiveness in a clinically meaningful way, 
the answer to this question will be yes. Possible differences might include, but are not 
limited to, differences in human factor issues (e.g., self-administration versus 
administration by a guardian), reference or normal values, size, scaling of the device, 
blood sampling or sample quantity issues for in vitro diagnostic devices, energy, delivery, 
device function, or device materials. This question is also related to whether conditions 
for preclinical or clinical testing differ between adult and intended pediatric 
(sub)population(s) and whether the device needs to change over time to accommodate 
growth and development.  Sometimes device characteristics and patient characteristics 
are intertwined.  For example, if the normal value (for diagnostics) or performance (for 
therapeutics) of the device depends on a body measurement or unique physiology that 
differs between adult and intended pediatric (sub)population, then the device 
measurement could differ. 
 
If the answer to question 2 is yes, then full extrapolation is not feasible.  However, as 
described above, partial extrapolation may still be viable in certain circumstances.  
 
Question C-3: Are there characteristics unique to the intended pediatric 
(sub)population(s) that could impact either the effectiveness or safety of the device when 
used in the pediatric (sub)population(s) in a clinically meaningful way? 
 
Some devices might require special considerations that affect only pediatric patients; for 
example: 
 
  Growth of the child during the device performance period 
  Specimen sample size or quantity 
  Unavailable or inconsistent reference or normal valuesFor serologic in vitro 
diagnostic devices, specific challenges in certain subgroups due to differing 
immune status 
  Analytical issues which affect interfering substances for in vitro diagnostic 
devices 
  Drug dose or metabolic differences for therapeutic drug monitoring devices 
  Pediatric human factors 
  Increased impact of time exposure to younger subjects (e.g., long-term toxicity 
differences between populations)   
 
19 
 
  Contains Nonbinding Recommendations 
 
The kinetics or physiology might differ between adults and children, which might then 
influence the interpretation of test results or treatment modality, ultimately impacting the 
effectiveness or safety of the device across populations.  
 
An example where patient characteristics might affect interpretation of data concerning 
device effectiveness is a device indicated for weight loss. In this case, an adolescent and 
an adult may have different body sizes and/or masses that may impact evaluation of a 
devices effectiveness. For an adolescent study subject, weight gain could be attributed to 
normal growth, whereas for an overweight adult, weight gain would more likely 
demonstrate the failure of the device to have its intended effect. 
 
If the answer to question C-3 is yes, full extrapolation is probably not feasible. However, 
as described above, partial extrapolation may still be viable in certain circumstances. 
 
Question C-4: Are there differences in disease characteristics between adult and pediatric 
(sub)population(s) that could impact either device safety or effectiveness in the pediatric 
(sub)population(s) in a clinically meaningful way? 
 
For some devices, there might be differences in disease characteristics between adults 
and children that are highly likely to affect how the device performs or how test results 
are interpreted. The prevalence or severity of disease characteristics might differ between 
adults and children, or the natural course of the disease might differ. For example, a 
diagnostic device could indicate the need for medical intervention differently for children 
than for adults because analyte levels considered safe may differ for each population.  
 
If the answer to question C-4 is yes, full extrapolation is probably not feasible. However, 
as described above, partial extrapolation may still be viable in certain circumstances. 
 
Question C-5: Are there other differences between adult and pediatric (sub)population(s) 
that could impact either device effectiveness or safety in the pediatric (sub)population in 
a clinically meaningful way?  
 
This question allows for consideration of other differences that are not addressed by the 
first four questions.  
 
If the answers to questions in Box C are all no, and if the adult data are of sufficiently 
high quality, then full extrapolation could be considered, and it is possible that no 
pediatric data would be needed to consider approval for the pediatric indication. 
 
Study design and sampling plan are factors that could influence data quality. A registry or 
single-arm study is of lower quality than a randomized controlled (and blinded) trial. 
Responses from registries or single-arm studies may be biased in favor of the device 
because the subjects know they are receiving a new treatment that they hope to be better 
than the current standard of care. Allowing study subjects to choose their own treatment 
arms instead of randomly assigning them to treatments may be similarly biased. The 
Guidance for Industry, Clinical Investigators, and Food and Drug Administration 
20 
 
  Contains Nonbinding Recommendations 
 
StaffDesign Considerations for Pivotal Clinical Investigations for Medical Devices,15 
issued in 2013, compares study designs in terms of general quality, and represents the 
agencys proposed approach on this topic.  
 
If the answer to any or all of questions in Box C is yes, then the decision on whether 
partial extrapolation is appropriate will depend on whether some prospectively collected 
pediatric data can be obtained and/or whether an appropriate statistical model can be 
constructed such that pediatric outcomes can be predicted reliably by borrowing strength 
from the adult data. As stated above, statistical models may be used to combine relevant 
adult data with pediatric data in order to increase precision in inferences made from a 
pediatric study. These models can then potentially account for differences identified in 
the decision tree (see Section 7 and Appendix B for a discussion). In this way, the 
borrowed or extrapolated data have the potential to be used in some capacity to fairly and 
responsibly conclude that there is reasonable assurance of the effectiveness and/or safety 
of the device.  If it is determined that existing clinical data cannot be fairly and 
responsibly used in some capacity to conclude that there is a reasonable assurance of 
effectiveness and/or safety, extrapolation should not be considered. 
 
It is important to reiterate that any anticipated differences between adult and pediatric 
populations may not be realized until after the pediatric study is finished, if a study is 
recommended. Therefore, the realized extent of partial extrapolation is determined after 
data become available, and the statistical model is fit to the adult and pediatric data. 
 
If there are other device- or disease-specific questions not addressed in the Pediatric 
Extrapolation Decision Tree that could assist the FDA review team in its review, those 
questions may also be considered under Question C-5 in the tree. These situations may be 
more complex and require thoughtful collaboration between the FDA review team and 
the sponsor to determine whether extrapolation might be feasible. Borrowing of data may 
be achieved for some areas, while the sponsor may need to collect data in other areas. See 
Appendix A for examples. 
7. Factors That Could Limit Extrapolation 
 
This section describes a series of general factors that can aid in determining whether, and 
to what extent, extrapolation is appropriate.   
 
Factors that may preclude extrapolation of any adult data include but are not limited to 
the following: 
 
  There is little knowledge of the disease or condition in pediatrics. 
                                                 
15Available at 
http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm265553.htm. 
 
21 
 
  Contains Nonbinding Recommendations 
 
  The device is not FDA-approved or -cleared for adults. 
  Endpoints cannot be directly borrowed. 
  Statistical models cannot account for differences. 
  Human factors and growth can affect safety in pediatric patients (these factors 
dont exist in adults). 
  Appropriate labeling cannot be written for the pediatric population or 
subpopulation(s) targeted.  
  The practice of medicine has changed since the device was initially approved to 
such an extent that historical data would likely be different than prospectively-
collected data. 
  Appropriate risk mitigation cannot be assured.   
 
Factors that may limit extrapolation to a partial extent and thus require conducting a 
prospective study of pediatric patients include, but are not limited to, the following: 
 
  The age difference between the pediatric (sub)population and the available adult 
data is too great, making it difficult to infer similarity in risk or effectiveness.  In 
such cases, it may be more appropriate to extrapolate to a pediatric age that is 
closer to the mean age of the adult population.  For example, it might be more 
appropriate to extrapolate young adult data to an adolescent indication than to a 
neonate indication. 
  Other supportive pediatric data are outdated and may not properly represent 
current treatment trends and practices.  
  There are important differences between the adult and pediatric 
(sub)population(s) such that the adult data cannot substitute for data from a 
prospective pediatric study to fairly and responsibly conclude that there is 
reasonable assurance of the safety and effectiveness of the device in the pediatric 
population. 
 
Whether any of these factors would preclude extrapolation or limit it to a partial extent 
depends on how the differences are expected to influence potential conclusions of the 
new study. 
8. Uncertainty in Extrapolating Data  
 
Extrapolation does add uncertainty into FDAs assessment of the effectiveness and safety 
of a device. Whether extrapolating partially or in full, there remains some uncertainty 
even though statistical modeling may be used to account for observed differences and 
increase precision of inferences.  The extent of this uncertainty depends on the 
differences between the two populations and the quality of the data. FDA considers this 
uncertainty as a factor when making benefit-risk determinations. 
22 
 
  Contains Nonbinding Recommendations 
 
 
FDAs Guidance for Industry and Food and Drug Administration Staff Factors to 
Consider When Making Benefit-Risk Determinations in Medical Device Premarket 
Approval and De Novo Classifications16 should be consulted to understand how 
extrapolated data might be weighed within a benefit-risk framework when considering 
device approval.  Because there may be greater uncertainty when using borrowed data, it 
may not carry the same weight as stand-alone pediatric studies.   
 
Regardless of the method used, extrapolation will only be permitted when it can be done 
in a manner that supports reasonable, scientifically sound conclusions about medical 
device effectiveness and safety based on valid scientific evidence. 
9. Statistical Methodology for Extrapolation 
 
When the use of extrapolation is determined to be appropriate, a sponsor may have 
several options for how to extrapolate the adult data. Available options could depend on 
whether a prospective study of pediatric patients is needed and feasible, and/or whether 
sufficiently robust pediatric data can be obtained in other ways, such as from prior studies 
run by the sponsor, studies in the literature, or pediatric registries.   
 
Many of the methods available for borrowing strength across studies employ the 
Bayesian approach to statistics, which espouses learning from evidence as it accumulates. 
Bayesian statistics use Bayes theorem to combine prior information with current 
information on a quantity of interest such as the primary endpoint. The idea is to consider 
the prior information and the current study results as part of a continuous data stream in 
which inferences are being updated each time new data become available. Prior 
information typically comes from results of previous comparable studies. Therefore, 
Bayesian methods are quite applicable for partial extrapolation from prior adult studies. 
Refer to FDAs Guidance for the Use of Bayesian Statistics in Medical Device Clinical 
Trials,17 issued in 2010, for an introduction to and more details on Bayesian statistics in 
medical device studies, including Bayesian hierarchical modeling, described briefly 
below because of its potential for borrowing across several studies.  Other methods are 
mentioned in Appendix B.  Although we mention some methods specifically, FDA may 
consider alternative methods suggested by applicants.  It is important to note that while 
Bayesian methods can be used, direct prior-to-posterior inference may not be applicable 
because that analysis entails pooling the prior and current data together.  Hierarchical 
                                                 
16 Guidance for Industry and Food and Drug Administration Staff Factors to Consider When Making 
Benefit-Risk Determinations in Medical Device Premarket Approval and De Novo Classifications,  
Available at 
http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UC
M296379.pdf. 
   
17 Available at 
 
http://www.fda.gov/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm071072.htm   
23 
 
  Contains Nonbinding Recommendations 
 
modeling, as well as other methods mentioned in Appendix B, do not directly pool all 
patients together. 
9.1 The Bayesian Hierarchical Model and Exchangeability of 
Studies 
 
Bayesian hierarchical modeling may allow an increase in effective sample size in a new 
study by borrowing strength (information) from prior studies. With a hierarchical 
model, as the differences among the study results decrease, more information is borrowed 
among studies, and a smaller sample size may be needed for the new (pediatric) study. A 
typical hierarchical model might have two levels: a patient level and a study level. In a 
two-level structure, studies have different but related treatment effects (e.g., mean 
differences between treatment and control group) or mean outcomes. The relationship 
among the studies is referred to as exchangeable studies, and has a mathematical 
definition described in more detail in FDAs Guidance for the Use of Bayesian Statistics 
in Medical Device Clinical Trials referenced above. A practical definition of 
exchangeable studies is that one could not distinguish the studies only by looking at the 
study results because there is nothing known a priori implying that one study achieved a 
better average device outcome than any other study.  For a two-level hierarchical model, 
study treatment effects or means are exchangeable, and patients are exchangeable within 
studies. It is important to note that patients are not assumed to be poolable across studies. 
 
The assumption of exchangeability facilitates borrowing across studies in a hierarchical 
model. Statistically, exchangeability implies that the variability of responses within each 
study is comparable (similar magnitude) to the difference in responses among the studies. 
This assumption might not hold for extrapolation because adults and children could 
respond differently to a treatment, and so the responses among studies could be quite 
different than the responses within each study. If this is true, then a weaker form of 
exchangeability (partial exchangeability, discussed in Section 9.2) may hold.  Ultimately, 
the actual extent of borrowing will depend on the data within the model. Therefore, if the 
device effect is actually observed to differ between adult and pediatric studies, the studies 
will not borrow much from each other, and the extent of extrapolation will be limited.  
 
In order to determine whether studies are likely to have exchangeable device effects, the 
FDA review staff and sponsors should identify differences in the studies that could hinder 
exchangeability. They should compare previous studies with the proposed study for 
similarity in relevant factors, including the following: 
 
 
  Device used 
  Patient population, including anthropometric measurements, when relevant 
  Protocol 
  Inclusion/exclusion criteria 
24 
 
  Contains Nonbinding Recommendations 
 
  Prognostic factors 
  Patient management 
  Ability of the patients to comply with instructions for safe and effective device 
use 
  Proximity in time 
  Operator training/experience   
 
Exchangeability is assessed by the clinicians and engineers from FDA as well as the 
sponsor. The sponsor should be prepared to discuss exchangeability or partial 
exchangeability among studies given covariates.  Partial exchangeability may still hold 
even if differences in any of the above factors (or others) limit or preclude the assumption 
of complete exchangeability of adult studies with the proposed pediatric study. However, 
if the identified differences are known to be associated with one or more measured 
variables, and the measured variables have sufficient overlap between populations, 
adjustments can be made to a hierarchical model so that the studies might still be 
exchangeable after accounting for those variables. The next section provides an overview 
of one commonly-used adjustment when the adult and pediatric studies have differences 
that affect the outcome of the study.   Appendix B provides more statistical details as well 
as other approaches to adjustment. 
9.2 Age-Related Covariates Associated With Device Outcomes 
 
As mentioned above, there are likely to be one or more differences that could prevent the 
assumption of exchangeability between adult and pediatric studies. If these differences 
can be identified and measured, it is straightforward to account for them in a hierarchical 
model. When this is done, we can say that the studies are exchangeable, except for 
measured differences on certain variables. Often the differences will be related to the size 
or ongoing growth of the patient. A simple example might be a new limb prosthesis. The 
effectiveness and safety of the prosthesis might differ depending on the size or weight of 
the patient. However, within a given patient size (e.g., height), the performance 
characteristics might be the same, regardless of whether the patient is an adult or child. 
 
It is imperative that FDA clinical reviewers and sponsors identify covariates that are 
associated with device performance and that might be responsible for any perceived 
differences in outcome for adults versus children or adolescents. A first step after 
identifying potential covariates associated with device performance is to determine how 
the covariate affects the primary outcome of the study, and then how age of the patient is 
related to the covariate.  Identified covariates should have sufficient overlap between 
adult and pediatric populations so that the relationship between the covariate and age on 
study outcome can be connected across populations. 
 
For example, a device whose effect is related to hormone level may have very different 
magnitudes of effect for adults than for children because they have different circulating 
hormone levels. If patients are categorized into low, medium, and high hormone levels, 
25 
 
  Contains Nonbinding Recommendations 
 
then within each category, the adult studies might be exchangeable with the pediatric 
study. Presumably, if hormone level is highly associated with the effect of the device, the 
sponsor is likely to have patient-level data on the level of the circulating hormone in 
adults. Patient-level information in children would enable the sponsor to construct a 
model that relates hormone level in the blood to outcome, and thus condition on hormone 
level to assume exchangeability. By conditioning we mean that except for hormone 
level, there are no known (and measured) differences between adults and children that 
would allow one to identify an outcome as belonging to either an adult or pediatric 
patient. If there were, then these measured covariates would also be added to the model. 
The structure of the model would be agreed upon by both the sponsor and FDA. 
Moreover, once data become available, the assumed model would be checked against the 
data to ensure it is still valid. 
 
When premarket pediatric data are needed, there are several suitable study designs and 
analyses to consider, depending on circumstances related to the feasibility of collecting 
the data. The Guidance for Industry, Clinical Investigators, and Food and Drug 
Administration StaffDesign Considerations for Pivotal Clinical Investigations for 
Medical Devices,18 issued in 2013, discusses several concepts and principles related to 
designing medical device studies.  
 
                                                 
18 Available at 
http://www.fda.gov/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm373750.htm 
This guidance represent FDAs current thinking on this topic. 
 
26 
 
  Contains Nonbinding Recommendations 
 
Appendix A. Examples of the Decision Process for 
Extrapolation 
 
The examples in this section are intended to demonstrate the use of the Pediatric 
Extrapolation Decision Tree. The examples are not predictive of FDA decisions but may 
be considered guides for how FDA weighs the appropriateness of extrapolating existing 
clinical data to support pediatric indications. 
 
A.1 A Hypothetical Example of Full Extrapolation for 
Effectiveness 
 
A gel that is used as a pleural air leak sealant is proposed to be indicated for a pediatric 
population (aged 221). The gel is currently approved for adults in the closure of 
remaining visible air leaks incurred during open resection of lung parenchyma, after 
standard sutures have been applied; the same condition can occur in pediatrics. Suppose 
that extrapolation of effectiveness from adults to pediatrics is under consideration. The 
measure of device effectiveness used to gain approval was that the patient remained free 
of air leaks 1 month post-surgery, after application of the gel. The same measure would 
be used for pediatrics. Therefore, the first two questions in the decision tree (A and B) are 
answered yes. 
 
The gel is intended to be applied in the body in the same location for both age groups, for 
roughly the same duration (eventually the gel gets resorbed and excreted). Furthermore, 
the gel itself does not have different characteristics for adults than for children. With 
respect to the purpose of the gel, the disease characteristics (air leaks) are similar for both 
adult and pediatric patients. However, the size of the air leak and therefore the amount of 
gel used and perhaps the size of the syringe to deploy the gel could differ between adult 
and pediatric patients.  In this example, the Agency has determined that these differences 
do not impact device effectiveness in the pediatric population in a clinically meaningful 
way. The gel has been demonstrated to be equally effective when covering smaller areas 
as larger areas, and the size of the syringe is not relevant to effectiveness. Therefore, the 
answers to Box C were all NO, and full extrapolation of effectiveness data could be 
considered for this device. In this case, the FDA might decide that adult effectiveness 
data could be substituted for prospective pediatric study (i.e., full extrapolation) if the 
adult studies are of sufficient quality.  
 
In separately assessing whether the existing data could be extrapolated to demonstrate 
safety in the pediatric population, the potential for adhesions was felt to be of concern 
due to the expected needs for reoperation in this population, based on the preclinical 
testing results.  For this reason, safety extrapolation was not performed and a separate 
study for safety in pediatrics was recommended. 
 
27 
 
  Contains Nonbinding Recommendations 
 
A.2 A Hypothetical Example of Partial Extrapolation with 
Relevant Age-Associated Differences between Populations 
Accounted for Via Modeling 
 
A diagnostic device is approved in adults as an aid to diagnosing a particular disease or 
condition through the quantitative measurement of a particular measurand. This 
measurand is the same one used to diagnose both adults and children.  In the adult study, 
the device was compared to the currently used diagnostic test, which is generally 
considered a reference standard method, to provide reasonable assurance of safety and 
effectiveness. This reference standard method requires the collection of a large amount of 
blood. An indication is sought for pediatric patients as young as 2 years old.  Use of the 
reference standard method as the comparator for the pediatric population was considered 
an unsafe option, due to the need to collect large amounts of blood from young children.   
 
When referencing the flow chart to decide whether or not extrapolation is appropriate, it 
is apparent that the condition occurs in both adults and pediatrics and that there is an 
endpoint that is relevant to both populations. It is not known whether the values obtained 
from the comparator reference standard are the same between adult and pediatric patients.  
Because these values could differ, the difference in results between the device and the 
comparator method may have a different magnitude for adults than for children.  
Accordingly, the difference in blood volume that precludes use of the reference standard 
method as the comparator is a unique characteristic of the intended pediatric population, 
which could have a clinically meaningful impact on the safety or effectiveness of the 
device.  Specifically, the difference in the use of the reference standard may change the 
diagnostic result which, if erroneous, could impact patient safety.  Therefore, full 
extrapolation is not appropriate.  We thus proceed to consider whether partial 
extrapolation is appropriate.   
 
In this example, the device characteristics, device matrix, and interfering substances are 
considered the same for the adult and pediatric population.  It is also known that the 
reference standard values expected for adults and children can be calibrated to be 
comparable by accounting for body size, among other measured patient-level variables 
that may be correlated with age. Because calibration using measured variables is possible, 
the Agency and sponsor agree that the adult reference standard data can be borrowed 
statistically to bolster the expected reference information in pediatrics. To the extent that 
the calibrated reference standard values are similar between the adult and pediatric 
populations, more adult data can be borrowed.  Therefore, because the observed 
differences between the adult and pediatric populations can be accounted for in a 
statistical model, the extrapolated data may be used in support of demonstrating a 
reasonable assurance of the safety and effectiveness of the device.  Because the data from 
the adult population were of high quality in terms of study design, these data are 
considered a viable candidate for partial extrapolation. 
 
This example highlights that borrowing from adult data can be done not only for the 
device group in a clinical study, but also for control groups or reference standard values.  
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In many cases, a control or comparator is not available for pediatrics but it is available for 
adults. As illustrated, partial extrapolation can potentially be used in these cases. 
A.3 A Hypothetical Example of Partial Extrapolation  
 
Suppose a company wishes to extend an indication to adolescents for their marketed 
device X to treat a condition Y.  The device is approved for use in adults.  There are 
several available adult data sets from the US pre-market application as well as from 
marketing applications in other regions of the world.  The endpoint used in the available 
studies is identical to the endpoint desired in the adolescent population.  However, the 
adults were followed for eight months, and the FDA recommends following adolescents 
for at least 12 months.  There are no other identified differences between populations 
with respect to the anticipated effectiveness or safety of the device.  Thus, Box C has 
been answered no.  However, full extrapolation is not recommended because the eight-
month adult data are not sufficient to serve as a substitute for twelve-month pediatric 
data. 
 
Based on additional information from studies published in medical journals about how 
the device performs beyond eight months in adults, the sponsor was able to borrow from 
the adult studies and use statistical modeling to predict adolescent response at 12 months.  
The predictive model also incorporated some prospectively collected adolescent data out 
to 12 months.  Thus, the data quality when paired with the statistical model was 
determined to be sufficient to allow for partial extrapolation.  However, with the 
leveraged adult data the sample size estimated for the adolescent study was smaller than 
it would have been otherwise.  Once the adolescent study is completed, the model will be 
verified to ensure that assumptions are met and borrowing is indeed appropriate.  
A.4 Hypothetical Examples where Extrapolation is not 
Recommended 
 
A.4.1 Hypothetical example where extrapolation is not recommended because of 
quality of data 
  
A pre-amendment device is not indicated for pediatric use. When submitting their 
devices annual report to FDA, the sponsor cites case report studies which the sponsor 
believes suggest an indication for pediatric use may be appropriate.  The disease to be 
treated is essentially the same in adults and pediatric patients, and the endpoints used to 
evaluate clinical outcomes are also the same. There are also no apparent expected 
differences between the pediatric and adult response to device. Therefore, the answer to 
Box C is no.   
 
However, the adult data available for extrapolation are decades old.  Both the practice of 
medicine and relevant study design considerations have significantly changed.  As such, 
despite the similarities between the adult and pediatric populations, it is likely that FDA 
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would determine that the adult data in this case are not of sufficient quality for either full 
or partial extrapolation.  
 
A.4.2 Hypothetical example where extrapolation is not recommended because of 
relevant differences  
 
Consider a generic device which is indicated for a rare adult disease. A sponsor would 
like to extend the indication to a pediatric subpopulation because the endpoints between 
the two groups are similar. The only difference in the response to device intervention 
relates to how pediatric growth may impact the safety and effectiveness of the device. 
However, the device may need to be removed or adjusted for growth, which requires 
surgical intervention and introduces additional risk for pediatric patients.  In addition, the 
anticipated impact of pediatric growth on device safety and effectiveness is largely 
unknown, and there is limited clinical experience in adults so the data are not sufficient to 
reliably inform modeling.  Partial extrapolation is not feasible because the differences 
between the adult and pediatric populations cannot be accounted for, clinically or with 
modeling.  Therefore, extrapolation is not recommended in this scenario. 
A.5 An Example of an Actual Extrapolation 
 
Patients with systemic, left-sided, congenital heart valve disease pose significant 
challenges for physicians.  There are limited technological solutions available for these 
patients. Few replacement heart valves are indicated for pediatric patients, and 
commercially available bioprosthetic valves for aortic and mitral valve replacement may 
not be available in sizes appropriate for infants and children. 
 
The clinical impact of congenitally deformed valves is significant and often lifelong. 
Treatment decisions are almost always impacted by the effects of rapid growth,  
active lifestyle, and accelerated deterioration of biological prostheses. Pediatric valve 
replacement is a high-risk procedure involving higher operative mortality, high 
reoperation rate, and late morbidity compared to adult patients undergoing the same 
operation. 
 
The reasons for the higher operative mortality are multiple and complex. Most often, the 
available prosthesis is too large for the childs anatomy, resulting in delay in referral for 
surgery. When surgery is undertaken, additional steps are often required to enlarge the 
site of implantation to accommodate the prosthesis.  
 
Clinical studies have routinely been conducted on the adult patient population. However, 
pediatric patients have typically been excluded from replacement heart valve trials for 
several reasons, including: 
 
  Since pediatric heart valve replacement is a relatively rare surgical procedure, 
there is a limited patient pool requiring a replacement heart valve, which can lead 
to prolonged recruitment to achieve required enrollment numbers 
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  Pediatric patients often have? complex health histories (many leading to early 
mortality) 
  Co-morbidities confound the adverse event profiles for the study, making it very 
difficult to assess overall safety of the valve 
  There are limited valve sizes available 
  Following valve replacement, the pediatric patient continues to grow, ultimately 
necessitating reoperation and the placement of a larger valve  
  Uniformity of an identifiable patient population is extremely challenging to 
achieve, again leading to prolonged study recruitment 
 
Below we trace the pathway to extrapolation of adult data using the decision tree, 
beginning with effectiveness. First, the disease is identified as being the same for 
pediatrics as for adults. Additionally, the primary endpoint for effectiveness would be 
similar in both a pediatric study and adult study (Decision Tree Questions A and B). 
Therefore, the adult data are considered relevant. 
 
While a heart valve for a pediatric patient is implanted in the same location as for an 
adult, the duration of implantation of a particular size will be shorter for a pediatric 
patient due to normal pediatric patient growth.  This could influence the effectiveness of 
the device for pediatric use.  Therefore, the answer to question #1 in Box C is yes.  
Furthermore, one of the most important patient characteristics unique to pediatrics is that 
the patient continues to grow after valve replacement, necessitating additional operations 
to implant larger valves. This difference can also influence effectiveness.  Question #3 is 
also answered yes because pediatric patient growth could impact effectiveness of the 
heart valve.  Therefore, there are some differences related to effectiveness. 
 
However, such differences can be explained clinically as associated with valve size rather 
than age per se.  Additionally, there is extensive relevant adult data of sufficient quality 
available for the sizes of interest and the different positions (aortic, mitral) to inform a 
statistical model to account for the differences.  It was thus possible to incorporate a 
clinical relationship between valve size, position and device effectiveness into a statistical 
model that could be used for extrapolation, the data from which could meet the threshold 
for valid scientific evidence and be used to support an effectiveness determination.  
Therefore, a partial extrapolation was considered plausible for effectiveness.  FDA 
agreed that a sample size of 15 pediatric patients per size per position (aortic, mitral), 
when combined with the borrowed adult data, could potentially suffice for demonstrating 
clinical effectiveness of the device for the proposed pediatric indication.   
 
In assessing whether the existing data could be leveraged to extrapolate for safety, the 
primary difference with pediatric device use is that patient growth after valve 
replacement necessitates additional operations to implant larger valves.  As such, the 
answer to question #2 in Box C would be yes.  This exposes pediatric patients to 
additional operations, which pose an incremental risk.  Therefore, safety data adequate to 
evaluate this incremental risk for pediatric patients was necessary.  FDA concluded that 
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the number of pediatric patients that would be prospectively enrolled to confirm 
effectiveness would be sufficient to evaluate safety as well.  In addition, a post-approval 
study was recommended to assess the long-term safety and effectiveness of the device in 
pediatric patients. 
 
This example illustrates how available relevant adult clinical data were leveraged to 
bolster new pediatric data in a manner that meets the threshold for valid scientific 
evidence.  When considered in conjunction with safety evidence that had already been 
compiled (e.g., preclinical testing, engineering models, biocompatibility.) and the data 
derived from prospective pediatric studies, appropriate partial extrapolation was used to 
support a safety and effectiveness determination for new pediatric heart valves.   
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Appendix B: Details on Statistical Modeling for 
Extrapolation 
 
In this appendix, we present further details of statistical modeling that might be 
performed for partial extrapolation. As described in the text, a goal for partial 
extrapolation is to borrow strength or information from adult data while still accounting 
for the important differences between adult and pediatric populations. Accounting for 
baseline characteristics is a common way to distinguish studies that should not be 
considered exchangeable. This technique was described above. However, if there are 
multiple adult studies from which to borrow, then placing another level in the two-level 
hierarchy to include subgroups of studies might further lessen borrowing between adult 
and pediatric studies when they should not be considered on the same level. 
 
We introduce a simple three-level hierarchical model, followed by an overview of other 
possible methods for borrowing strength along with pros and cons of the methods. As 
mentioned in Section 9, FDA may consider alternative methods suggested by sponsors, or 
may suggest methods not covered in this guidance document. 
B.1 A Three-Level Hierarchical Model 
 
In the proposed three-level hierarchical model (see Figure 2), the third level involves the 
two patient populations (adults and children), each having studies that are exchangeable 
with one another. The adult studies are exchangeable among themselves, and the 
pediatric studies are exchangeable among themselves. To facilitate borrowing between 
the adult and pediatric studies, they are connected by assuming exchangeability between 
the two patient populations regarding the device effect on the endpoint of interest. That 
is, prior to knowing anything about what type of effect a device will have, it is presumed 
that if there is evidence of the effect of the device on a population, it would not be 
possible to tell which population it was, adult or pediatric.  
 
 
 
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Figure 2. Three-Level Hierarchical Model Structure Example: Studies Within 
Patient Populations Have Different But Related Effects 
 
  Patient Populations 
 
  Pediatrics 
Level 3:  Adults 
 
  Level 2:  Study 1  Study 2  Study 3  New  Study 
 
y ,...,y   y ,...,y   y ,...,y   y  
  Level 1:  1,1 1,n1 2,1 2,n2 3,1 3,n3 new
   
  Level 1: Patients (y) exchangeable within studies 
Level 2: Studies exchangeable within patient populations 
 
Level 3: Patient populations are exchangeable 
 
In Figure 2, patients are represented by their values (y) on the endpoint of interest. There 
are n  patients in Study 1, n  in Study 2, and n  in Study 3. (For simplicity, the figure 
1 2 3
represents single-arm studies). The adult population produced Studies 1 and 2, and the 
pediatric population produced Study 3. The two patient populations are assumed to come 
from a common super-population of patient populations. The figure also includes a 
branch for a future pediatric patient from the pediatric population. With Bayesian 
hierarchical models, not only is it possible to borrow strength to estimate individual study 
means and their population means, but it is also possible to estimate a predicted value for 
a new pediatric patient from the pediatric population, using the Bayesian predictive 
distribution. The Bayesian predictive distribution is the distribution of an unknown 
outcome, which can potentially be observed in the future. It is essentially the posterior 
distribution of a yet to be observed outcome (Carlin & Louis, 2009). 
B.2 Age-Related Covariates Associated With the Device Effect 
or Outcome 
 
Figure 2 above is highly simplified because it assumes no differences across patient 
populations that would affect the safety or effectiveness of the device. As with the two-
level model, in practice, there are likely to be one or more differences that could prevent 
the assumption of exchangeability between adult and pediatric populations (the third 
level in the hierarchy). If these differences can be identified and measured, it is 
straightforward to account for them in the model. Essentially, the model will dictate that 
the populations are exchangeable, except for measured differences on certain variables. 
Differences could be static or dynamic (time-varying) over the trial period. Often the 
differences will be related to the size or growth of the patient. The structure of the model 
should be agreed upon by both the sponsor and FDA. Moreover, once data become 
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available, the assumed model would be checked against the data to ensure it is still valid.  
Section 9.2 also discusses accounting for covariates. 
 
B.3  Extrapolation from a Single Adult Study 
 
When extrapolating from adult studies, it is advantageous to have several prior studies to 
use in an analysis to facilitate more precise estimation of the device effect in pediatrics. 
However, it is often the case that only a single prior adult study exists. Although the 
example above described borrowing from two adult studies, similar methodology can be 
used when there is a single prior adult study available. FDAs Guidance for the Use of 
Bayesian Statistics in Medical Device Clinical Trials (2010) discusses limitations with 
the use of Bayesian hierarchical models with a single prior study.  As mentioned in 
Section 9, it is important to note that while straightforward prior-to-posterior inference is 
a cornerstone of Bayesian statistics, in the application of pediatric extrapolation, it may 
not be appropriate because it assumes exchangeability of pediatric and adult patients.  
That is, the analysis pools the prior and current studies together.  Often, if this method is 
used, the adult prior would be adjusted in some fashion to discourage pooling of patients 
together (e.g., via the use of a power prior, discussed shortly). 
 
Several authors have developed methods for incorporating a single historical study as 
prior information in a Bayesian model, where the weight placed on the historical study 
varies with the similarity of the historical study and the current study data as they are 
collected (e.g., Hobbs et al., 2011, 2012). Some of these methods have limitations similar 
to those of hierarchical models, in that fairly informative priors must be used to describe 
the relationship between the historical and current studies. However, the specification of 
the priors might be conceptually easier than it is with a hierarchical model. 
 
In limited cases it might be reasonable to pre-specify, as a percentage, the amount of 
borrowing from the prior adult data set(s). The method of power priors (Ibrahim & Chen, 
2000) uses a prior that is constructed from the likelihood of the prior data raised to a 
power, where the power falls between 0 and 1. The power indicates the downweighting 
of the prior data, so that a power of 0.5 implies that 50% of the information from the prior 
likelihood is borrowed. Unfortunately, when the power must be fixed in advance, it 
cannot change based on later observed data from a new pediatric trial. Placing a prior on 
the power parameter itself, thereby potentially allowing the data to determine the amount 
of borrowing, has been shown in practice to have limited success (see, for example, the 
discussion in Hobbs et al., 2011).  
B.4 Additional Methods for Extrapolation 
 
While Bayesian methods are described in this document, non-Bayesian methods can also 
be used for borrowing strength. The structure of the hierarchical model is not inherently 
Bayesian, and it can be used without the interpretation of posterior probability. However, 
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in many cases the overall conclusions will remain the same, and the Bayesian 
interpretation of posterior probability is often simpler to understand.   
 
As mentioned above, the Bayesian hierarchical model can be difficult to use when there 
is only one observed prior adult study. The between-study variance either must be pre-
specified (and just like with the pre-specified power parameter for the power prior, it 
cannot be changed once the pediatric trial is run), or an informative prior must be placed 
on the between-study variance, potentially limiting the range of values it can realize once 
the pediatric study is run.  In this case, the use of a pre-specified discounted informative 
prior may be more appropriate, and a discussion and agreement between the sponsor and 
FDA is recommended. 
 
In addition to hierarchical models, one could use propensity score methods for 
extrapolation from adult data (Rosenbaum & Rubin, 1983; Yue, 2007, 2012). In a 
situation where there is sufficient overlap between covariates for the pediatric and adult 
populations, propensity scores could be used to adjust for imbalances. A propensity score 
for a subject is the probability of the subject being assigned to the treatment group in a 
clinical trial, rather than to the control group, conditional on a set of measured baseline 
covariates (but not on the measured outcome variable). In a randomized trial, with 1:1 
randomization, this probability is by definition 0.5, independent of any covariates. In a 
nonrandomized study, the probability often depends on observed covariates. If it depends 
only on observed covariates, then for the same values on those covariates, two subjects 
have the same probability of being assigned to the treatment group. For a set of subjects 
with the same probability of receiving the treatment over the control, an estimate of the 
treatment effect will be unbiased, just as it would be in a randomized trial. Accounting 
for the propensity score in a weighted or matched analysis can then yield an overall 
estimate of the treatment effect that is unbiased despite unbalanced covariate 
distributions.  
 
If there is less overlap between the adult and pediatric propensity scores, an overall 
estimate of the device effect could possibly be obtained using regression adjustment 
based on the propensity score. In general, this adjustment is similar to that described in 
Sections 9.2 (Age-Related Covariates Associated With Device Outcomes) and B.3 
(Extrapolation from a Single Adult Study).  
 
The propensity score is a single representation of all measured baseline covariates. The 
single-dimensional representation makes it easy to use in modeling, but the form of the 
model might be more difficult to determine from a summary measure rather than from 
individual covariates. Moreover, there is no simple way to account for variability across 
studies that the hierarchical model can incorporate. 
. 
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Yue, L. Q. (2012). Regulatory considerations in the design of comparative observational 
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