Contains Nonbinding Recommendations 
Radiation Biodosimetry Medical 
Countermeasure Devices 
Guidance for Industry and  
Food and Drug Administration Staff  
Document issued on April 18, 2016. 
The draft of this document was issued on December 30, 2014. 
For questions about this document, contact the Division of Molecular Genetics and Pathology at 
301-796-6179 or Jennifer Dickey at 301-796-5028 or via email at Jennifer.Dickey@fda.hhs.gov. 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Devices and Radiological Health  
 
Office of In Vitro Diagnostics and Radiological Health 
Division of Molecular Genetics and Pathology Devices 
  Contains Nonbinding Recommendation 
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-2014-D-2065. Comments may not be 
acted upon by the Agency until the document is next revised or updated. 
Additional Copies 
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 
1400045 to identify the guidance you are requesting. 
  Contains Nonbinding Recommendation 
Table of Contents 
I.  Introduction ..............................................................................................................................1
II.  Background..............................................................................................................................2
III.  Scope ........................................................................................................................................4
IV.  General Considerations for Submission Contents...................................................................4
A.  Benefit-Risk Analysis.....................................................................................................4
B.  Device Description and Specifying the Intended Use....................................................5
1.  Stage of Response.......................................................................................................6
2.  Appropriate timeframes for testing .............................................................................6
3.  Assay limitations .........................................................................................................6
C.  Samples...........................................................................................................................6
1.  Sample availability......................................................................................................6
2.  Specimen collection and handling..............................................................................7
3.  Unirradiated samples..................................................................................................8
D.  Analytical Validation Studies.........................................................................................8
1.  Accuracy in relation to the dose delivered ..................................................................9
2.  Measuring interval or expected values.......................................................................9
3.  Interference ............................................................................................................... 10
4.  Other analytical testing protocols ............................................................................. 10
E.  Controls and Calibrators ............................................................................................... 11
F.  Instrumentation and Software ....................................................................................... 11
G.  Animal Study Considerations ....................................................................................... 12
1.  Defining an appropriate animal model ..................................................................... 12
2.  Effect of confounding factors on animal studies ...................................................... 13
3.  Bridging animal data to human data ......................................................................... 14
H.  Establishing Performance Characteristics  Clinical Validation Studies with Human 
Samples ................................................................................................................................... 15
1.  Prospective clinical studies ....................................................................................... 15
2.  Clinical studies using banked samples ...................................................................... 16
3.  Limitations of clinical studies ................................................................................... 16
I.  Labeling ........................................................................................................................ 16
1.  Instructions for use .................................................................................................... 17
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2.  Limitations ................................................................................................................ 17
3.  Interpretation of results ............................................................................................. 18
4.  Performance characteristics ...................................................................................... 18
J.  CLIA Categorization ..................................................................................................... 18
V.  Appendix A: Statistical Considerations for Biodosimetry Devices .......................................20
A.  Study Blinding (Masking) ............................................................................................ 20
B.  Confidence Intervals for Estimates of Performance ..................................................... 20
C.  Study Analysis .............................................................................................................. 20
1.  Quantitative, continuous, or semi-quantitative output .............................................. 22
2.  Qualitative output...................................................................................................... 22
3.  Analytical imprecision .............................................................................................. 23
4.  Missing data .............................................................................................................. 23
D.  Feature Selection During Algorithm Development ...................................................... 23
E.  Electronic Data .............................................................................................................. 24
  Contains Nonbinding Recommendation 
Radiation Biodosimetry Medical 
Countermeasure 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.  
I.  Introduction 
FDA has developed this guidance to provide industry and Agency staff with recommendations 
for the types of information that should be submitted to support marketing authorization (e.g., 
clearance or approval) for radiation biodosimetry medical countermeasure devices (referred to as 
biodosimetry devices or biodosimeters throughout this document).  This guidance applies to 
premarket submissions for medical device systems intended to measure biological responses to 
unintended (non-therapeutic) radiation absorption.  Biodosimetry devices are devices used for 
the purpose of reconstructing the ionizing radiation dose received by individuals or populations 
using physiological, chemical, or biological markers of exposure found in humans. Biodosimetry 
technologies may be used at various stages during triage, including both early mass casualty 
triage and subsequent clinical evaluation.  Such exposures could be the result of intentional harm 
or as a consequence of a disaster.  Devices may be designed to give quantitative outputs or 
qualitative information around a clinical decision making cut-point. Likewise, devices may be 
designed for use in field triage settings, at patient bedsides, or in Clinical Laboratory 
Improvement Amendments of 1988 (CLIA) certified clinical laboratories. FDA considered both 
high-throughput and single-use devices in developing this guidance document. 
This guidance document does not provide specific study designs; it describes design principles 
for studies that may be used to establish a reasonable assurance of the safety and effectiveness of 
biodosimetry devices. Sponsors should develop a validation plan to establish the analytical, pre-
clinical, and clinical performance characteristics in order to substantiate the claims in the device 
intended use statement, and discuss this plan with the FDA prior to beginning studies. Many of 
the considerations included in this guidance document assume that the biodosimeter output is 
related to a quantitative or qualitative measure of dose.  We acknowledge that there may be other 
relevant device outputs that manufacturers would like to pursue, such as risk of developing 
specific clinical symptoms (e.g., related to acute radiation syndrome). Many of the items 
discussed below can be applicable to a diversity of device outputs, if suitably addressed in a 
1  Contains Nonbinding Recommendation 
premarket submission. However, device outputs related to, or predicative of, clinical symptoms 
or outcome may be challenging to evaluate in the premarket setting because of the paucity of 
human data and challenge of translating results from animal studies to human use scenarios. In 
this case, we would recommend early engagement with FDA via the pre-submission process. 
Additionally, because it may be impossible to validate biodosimetry devices in the intended use 
population, sponsors should plan to include in their ultimate pre-market submission a plan or 
approach to conduct post-market testing when an emergency arises and their product is utilized 
in a real-world setting. Throughout this guidance document, the terms we, us and our refer 
to FDA staff from CDRH. You and your refers to the applicant or sponsor. 
FDAs guidance documents, including this guidance, do not establish legally enforceable 
responsibilities. Instead, guidances 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 means that something is suggested or 
recommended, but not required.  
II.  Background 
A radiological event, for instance from use of an improvised nuclear device or radiological 
dispersal device or as a consequence of a natural disaster that causes a large-scale industrial 
radiological accident, could potentially expose thousands of individuals to high levels of 
radiation that would require immediate assessment and medical intervention. A coordinated 
medical response including both early mass casualty triage and subsequent clinical evaluation 
would be important to mitigate harm resulting from the non-therapeutic exposure of individuals 
to ionizing radiation.1 Biodosimetry tools would be a critical component of such a response and 
will serve to differentiate the acutely irradiated population from the concerned public. Radiation 
biodosimetry estimates the absolute dose of radiation absorbed by an individual. Biodosimetry 
devices provide information that allows for the development of appropriate treatment plans for 
patients.  
The most significant benefit that biodosimetry has over physical dosimetry is that it takes into 
account the natural patient biological variability in radiation response, though physical dosimetry 
tools may provide a more accurate measurement of the actual radiation dose delivered. For 
example, a physical dosimeter such as a thermoluminescent dosimeter detects radiation on a 
physical substrate but does not assess the amount of radiation absorbed by the subject or the 
subjects biological response to radiation. Therefore, while a physical dosimeter may provide an 
accurate representation of dose, these devices will be unable to differentiate a patient who is 
radiation-sensitive (presumably needing a higher level of medical intervention) from a patient 
                                                 
1 For more information on the Federal government response plans for radiation disaster scenarios and a list of the 
applicable drugs and treatments recommended in such a scenario, see the document entitled Planning Guidance for 
Response to a Nuclear Detonation (http://www.remm.nlm.gov/PlanningGuidanceNuclearDetonation.pdf). This 
document was developed by the National Security Staff Interagency Policy Coordination Subcommittee for 
Preparedness and Response to Radiological and Nuclear Threats (last accessed on April 14, 2016). Note this website 
is not controlled by FDA.  
2  Contains Nonbinding Recommendation 
who is radiation-resistant. As such, the development of radiation biodosimetry techniques will be 
a powerful tool in personalizing therapeutic responses to radiation absorption, representing a 
significant benefit to public health.  
Most biodosimeters are in vitro diagnostic devices (IVDs), as defined in 21 CFR 809.3(a).2 A 
wide array of technologies may be employed to assess biological responses to radiation. 
Methodologies amenable to incorporation into biodosimetry devices could include nucleic acid 
based devices that utilize technologies such as polymerase chain reaction (PCR) or microarrays, 
devices designed to detect changes in protein expression using technology such as enzyme-
linked immunosorbent assays (ELISA) or flow cytometry, and other methodologies designed to 
detect other biological signals induced by absorption of radiation. 
Sponsors who intend to market biodosimetry devices must, in addition to other applicable 
requirements, conform to the general controls of the Federal Food, Drug, and Cosmetic Act 
(FD&C Act), and obtain marketing authorization (e.g., premarket clearance or approval) prior to 
marketing their devices. This guidance document represents our current thinking regarding the 
recommended information that should be included in a premarket submission of a biodosimetry 
device for assessing unintended absorption of radiation. We consider these recommended studies 
to be relevant for premarket notifications (e.g., 510(k) submissions or premarket approval 
applications (PMAs) that may be required for a particular device). The following documents may 
be useful in preparing premarket submissions:  
  Format for Traditional and Abbreviated 510(k)s 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocument
s/ucm084365.htm)  
  Acceptance and Filing Reviews for Premarket Approval Applications (PMAs) 
(http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/Guidanc
eDocuments/UCM313368.pdf) 
  In Vitro Diagnostic (IVD) Device Studies  Frequently Asked Questions 
(http://www.fda.gov/medicaldevices/deviceregulationandguidance/guidancedocuments/u
cm078309.htm)  
  eCopy Program for Medical Device Submissions 
(http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/Guidanc
eDocuments/UCM313794.pdf)  
Further information on device regulation can be found at Device Advice 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/default.htm). 
                                                 
2 In vitro diagnostic products are those reagents, instruments, and systems intended for use in the diagnosis of 
disease or other conditions, including a determination of the state of health, in order to cure, mitigate, treat, or 
prevent disease or its sequelae. Such products are intended for use in the collection, preparation, and examination of 
specimens taken from the human body. These products are devices as defined in section 201(h) of the Federal Food, 
Drug, and Cosmetic Act (the act), and may also be biological products subject to section 351 of the Public Health 
Service Act. 
3  Contains Nonbinding Recommendation 
FDA recognizes that biodosimetry device sponsors face unique challenges in attempting to meet 
premarket clearance or approval requirements for their devices. For example, ethical 
considerations limit the availability of appropriate clinical samples for biodosimetry device 
performance studies, leading to the expected use of animal models as surrogates for clinical 
validation purposes. As such, study designs that are not typically recommended for other IVD 
submissions may be appropriate for biodosimetry devices. The intent of this guidance document 
is to provide clarity to both industry and FDA staff on performance data and information that 
should be included in submissions to the Agency in order to help sponsors design studies and to 
facilitate our review process. Due to the devices novelty, the pre-submission process is an 
encouraged component of any biodosimetry device development. The pre-submission process is 
an opportunity for sponsors to get regulatory advice on their product development plan. The pre-
submission process, in general, leads to better prepared submissions and shorter review times. 
Information on the pre-submission process can be found in the guidance document entitled 
Requests for Feedback on Medical Device Submissions: The Pre-Submission Program and 
Meetings with Food and Drug Administration Staff 
(http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocu
ments/UCM311176.pdf). 
III.  Scope 
This guidance only applies to validation of diagnostic biodosimetry devices intended to be used 
to assess radiation absorption that occurs as a result of non-therapeutic or accidental exposures 
(e.g., a deliberate attack, such as use of an improvised nuclear device, or a natural disaster), and 
does not apply to medical devices intended to be used to measure doses delivered as a result of 
radiation therapy nor to devices that measure effects from long-term radiation exposure. In 
addition, dosimeters, which are devices that detect radiation exposure on a physical substrate 
rather than through a biological response and are worn by people who might be exposed to 
radiation during the course of their normal work (such as film badges), are not addressed in this 
guidance document. Finally, biological assays that might be used to detect the presence of 
ingested radioisotopes in sputum or urine are not considered in this guidance document. 
IV.  General Considerations for Submission Contents 
A.  Benefit-Risk Analysis 
A source of significant risk to patient health associated with biodosimetry devices is when failure 
of the device to perform as indicated leads to either deficient or inaccurate results or the incorrect 
interpretation of these results. Inappropriate or incorrect use of biodosimetry devices may be an 
additional risk. These potential risks may then lead to incorrect patient management decisions. 
For instance, a false positive result or overestimation of absorption in an emergency scenario 
could lead to unnecessary or inappropriate treatment for acute radiation syndrome3 or may result 
                                                 
3 Acute Radiation Syndrome (ARS), also referred to as radiation sickness, is an acute set of illnesses caused by 
irradiation of the entire body (or most of the body) by a high dose of penetrating radiation in a very short period of 
time (usually a matter of minutes). According to Centers for Disease Control and Prevention (CDC), ARS is 
 
4  Contains Nonbinding Recommendation 
in the patient being inappropriately placed in an expectant category and given only palliative care 
when treatment could be life-saving. However, a false negative result or underestimation of 
absorption could lead to failure to provide treatment or incorrect patient management which may 
be lethal. Thus it is essential to appropriately balance the benefits of biodosimetry devices with 
the risks, including the risk of incorrect results. 
Current laboratory methods for determining absorbed radiation doses utilize cell based assays 
that are accurate but take several days to complete. Therefore, part of evaluating the benefit-risk 
considerations for new biodosimetry devices includes consideration of the overall time to result 
or throughput of the system (e.g., decreased accuracy within a reasonable range may be 
acceptable for a device that provides a more rapid result in the context of triage). The guidance 
document entitled Factors to Consider When Making Benefit-Risk Determinations in Medical 
Device Premarket Approvals and De Novo Classifications 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm2
67829.htm) provides information on FDA benefit-risk determinations. Premarket submissions 
should include a discussion of the potential benefits and risks associated with the device in light 
of the biological radiation response pathway that is being assessed. This discussion should 
include but not be limited to the analytical strengths and weaknesses of the technology and the 
clinical information that is available demonstrating device effectiveness.  
B.  Device Description and Specifying the Intended Use 
All components of your biodosimetry system necessary to achieve the claimed functionality in 
the intended use statement should be listed in the device description.  
The intended use statement should specify: 1) the nature of the analyte (e.g., RNA, DNA, or 
protein), 2) specimen types in which testing may be performed (e.g., blood, urine, or saliva), 3) 
the specific population(s) for which the test is intended (e.g., pediatrics, general population), and 
4) the intended use setting(s) (e.g., field triage or professional use only). For the purposes of this 
guidance, field triage devices would be intended to be used by trained lay personnel (first 
responders) in a setting outside of a professional medical facility.  
The intended use statement should also explain whether the test is qualitative or quantitative and 
include any specific conditions of use. The intended use statement of most biodosimetry devices 
should also explicitly advise that results need to be considered in combination with other 
appropriate clinical signs and symptoms as well as radiation dispersal monitoring.  
Additional specific elements that should be considered and included as part of the intended use 
statement or in the device description for biodosimetry devices include the following: 
                                                                                                                                                             
classified into three classic sub-syndromes:  hematopoietic syndrome (H-ARS), gastrointestinal syndrome (GI 
syndrome), and cardiovascular/central nervous system syndrome (CV/CNS syndrome) 
(http://www.bt.cdc.gov/radiation/arsphysicianfactsheet.asp). Note this website is not controlled by FDA. 
5  Contains Nonbinding Recommendation 
1.  Stage of Response 
Biodosimetry devices may be designed for early field triage (usually qualitative). For these types 
of devices, the intended use should specify the throughput capabilities, time to result of the 
device, and the decision making cut-points assessed. Alternatively, biodosimetry devices may be 
designed for subsequent clinical evaluation for more definitive medical management, including 
dose level confirmation (usually quantitative or semi-quantitative). For these types of devices, 
specific clinical indicators of health status should be part of the intended use statement and, for 
quantitative devices, should include the assay measuring interval. 
2.  Appropriate timeframes for testing 
Many common biomarkers of radiation absorption display defined kinetics, during which they 
become detectable, remain stable, and then disappear from the matrix being examined.  
Therefore, it is important to specify the timeframe in which the device is designed to function, 
beginning from time of exposure. This should include both the beginning and end of the 
acceptable testing window (e.g., from 30 minutes to 48 hours post-exposure).  
3.  Assay limitations 
Validation of biodosimetry devices may be incomplete due to a lack of samples from the 
intended use population. Therefore, limitation statements may be needed to minimize risk of 
over-reliance on biodosimeter results when the real-world situation in which the device is being 
used does not mirror the scenarios tested. Limitation statements may be included in the intended 
use statement or in the warnings and contraindications section of the labeling, depending upon 
the risks associated with using the device in those limited situations. For instance, if validation 
testing was only performed on certain populations (e.g., not tested in pediatrics), for specific 
radiation types (e.g., gamma source only or limited to effects from low-linear energy transfer 
[LET] radiation), or on specimens or subjects exposed to limited doses and dose rates that may 
not reflect the final situation of use, these limitations should be captured in the labeling. Other 
situations that may cause inaccurate biodosimeter results (e.g., combined injury such as radiation 
plus physical trauma) should be taken into account when drafting an appropriate intended use 
statement. However, labeling limitations cannot generally be relied upon to justify missing 
validation studies for the intended use population. You should make every attempt to establish 
assay performance in the intended use population. If you determine that you are unable to 
perform validation using the intended use population, you should provide a justification along 
with a detailed description of the due diligence activities you performed to support your 
validation approach. 
C.  Samples 
1.  Sample availability 
We recognize the difficulty in acquiring appropriate clinical samples for use during device 
validation. Typically, the source of clinical samples is patient samples taken in the course of 
radiation therapy (partial or total body). However, these specimens may be difficult to obtain in 
6  Contains Nonbinding Recommendation 
sufficient amounts. Another challenge with these sources is that the therapeutic protocol restricts 
the radiation doses available (only low dose fractionated exposures may be readily obtained) and 
such specimens may not be sufficient to represent the spectrum of radiation absorption that 
occurs as a result of non-therapeutic or accidental exposures. In the absence of sufficient or 
appropriate clinical samples, contrived samples may be used for analytical performance testing, 
but only as a supplement to, and not as a substitute for clinical samples. 
Samples may be contrived by a variety of means including but not limited to: 
  ex vivo irradiation of the appropriate matrix,  
  spiking the analyte of interest into the appropriate matrix, 
  through the use of animal-derived specimens (e.g., specimens obtained from irradiated 
animals), as appropriate.  
In some instances, control materials (i.e., materials designed to serve as assay positive or 
negative controls) or contrived samples (i.e., samples designed to mimic a patient sample) may 
be used for the purposes of analytical performance testing. Premarket submissions should 
include a scientific justification for contrived sample utilization, a description of how contrived 
samples were generated and validated for testing, and a description of how the results obtained 
translate to the clinical setting. You are encouraged to discuss the most appropriate sample type 
for testing with FDA prior to designing your analytical validation studies. You should make 
every effort to obtain and use appropriate clinical samples to demonstrate performance.  
2.  Specimen collection and handling 
The quality and quantity of an extracted analyte can be affected by multiple factors such as 
specimen source, collection method, time of collection after exposure, and handling (e.g., 
transport, storage time, and temperature). Therefore, premarket submissions should include 
performance validation data to establish that the specimen collection and transport system 
employed by the device provides an adequate and appropriate yield of the analyte being detected 
by the assay (e.g., DNA or RNA from blood or tissue). Testing should also demonstrate that the 
device maintains acceptable performance under all the various specimen handling conditions 
claimed in the product labeling.  
Specimen stability should also be addressed in the premarket submission. However, we 
recognize there may be different analytical and clinical performance needs depending on whether 
the device is intended for early mass casualty triage in a field setting, for radiation absorption 
confirmation, or for dose refinement in a clinical laboratory setting. 
For example, if the device is intended to be used in an early mass casualty triage environment in 
a field setting, CDRH would evaluate whether the device is both sufficiently robust to withstand 
environmental impacts and appropriately user friendly for use by the intended user. Therefore, 
for biodosimetry devices intended for field triage use, testing should include performance testing 
to demonstrate the robustness of the device, and where relevant, performance testing to 
demonstrate the devices specimen collection and transport performance characteristics. By 
contrast, performance evaluation of devices intended for dose refinement may focus more on 
performance testing to demonstrate the devices measurement precision performance 
7  Contains Nonbinding Recommendation 
characteristics. In either case, specimen shipping stability performance testing will be critical if 
the testing is intended to take place far from where the patient samples are obtained. The 
acceptance criteria for all specimen stability parameters should be clearly indicated and justified 
in terms of the intended use environment as indicated in the labeling. 
3.  Unirradiated samples 
Unirradiated samples may be used both to assess the normal range (reference interval) or 
expected values for qualitative or semi-quantitative assays. In addition, these samples may be 
used to create contrived samples by spiking the analyte of interest into an appropriate matrix. 
Assessment of the reference interval or expected values of analyte expression should include a 
suitable number of normal samples to determine if biodosimeter analyte expression depends on 
subject age, race, gender, and common disease conditions (e.g., obesity, diabetes, or arthritis, as 
appropriate). Normal samples can be obtained prospectively or from appropriate sample banks. 
All normal samples should be collected as intended for your device. For instance, if blood will be 
collected in ethylenediaminetetraacetic acid (EDTA) tubes for use in your biodosimeter, then 
normal samples should be collected in EDTA tubes. As above, basic demographic information 
and information on disease conditions should be collected with normal samples. 
D.  Analytical Validation Studies 
Biological response to a radiation dose as measured by biodosimetry is confounded by patient-
to-patient variation in radiation response, making simple dose/response correlations difficult. As 
such, a well-documented explanation of the relevant biological pathways should be provided to 
FDA in order to justify a lack of correlation of the assay output to the radiation dose delivered 
attributable to natural biological response. Such information can be obtained through peer-
reviewed literature as well as bench testing. Well-controlled analytical studies should be 
provided to FDA to establish device performance across the entire measuring interval of the 
device in a defined sample subset. This analytical performance information will be critical to 
substantiate intended use claims of a biodosimetry device.  
Additionally, it is beyond the scope of this document to consider the broad range of analytical 
characteristics specific to each of the many and varied technologies that may be employed to 
develop biodosimeters. Therefore, you are encouraged to examine guidance documents that 
might be applicable to the type of technology your device employs to identify the types of 
analytical characteristics that might be appropriate to demonstrate for your device. For example, 
if a biodosimetry instrument utilizes a genetic test for heritable markers, you might consider 
consulting the Guidance on Pharmacogenetic Tests and Genetic Tests for Heritable Markers 
(http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocu
ments/ucm071075.pdf) to identify the types of analytical characteristics that might be 
appropriate to demonstrate for your device. 
Finally, appropriate standards documents drafted by the Clinical and Laboratory Standards 
Institute (CLSI) are additional helpful resources that might provide details on specific analytical 
performance testing appropriate for your device. Specific standards documents which might be 
applicable will be referenced throughout this section. A list of FDA recognized standards can be 
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found at the following website: 
http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfStandards/search.cfm.  
 
1.  Accuracy in relation to the dose delivered 
In order to demonstrate analytical accuracy, if the device output is radiation dose, then the 
devices measurement of the biological response to radiation will probably be compared to the 
physical calculated dose delivered. Therefore, the accuracy of the delivered dose is crucial, and 
the protocol for designing proper telemetry should be included in all study protocols. As 
discussed above, we expect that because the biodosimetry output will be confounded by the 
biological response to radiation, there will be between-individual variations which may 
complicate the correlation of the output to the radiation dose delivered.  
Statistical analysis plans, including acceptance criteria, should be developed around accuracy 
studies and be appropriate for the device output (qualitative or quantitative). Please refer to 
Appendix A in section V of this document for more information on statistical considerations for 
study designs. All accuracy studies should be designed to demonstrate device performance at the 
clinical decision making cut-points relevant to the intended use of your device (i.e., mass 
casualty triage or subsequent clinical triage for more definitive medical management) and 
contain pre-specified success criteria. The rationale for your acceptance criteria should be 
provided and clinically justified. 
A manufacturer may consider using a reference method to validate the accuracy of their 
biodosimetry device. For example, the dicentric chromosome assay (DCA) may be an 
appropriate reference method in some circumstances. However manufacturers should understand 
the limitations of available reference methods. For instance, DCA has several limitations, 
including that above 5 Gy the assay is no longer dose-dependent. Because of these limitations, 
sponsors may also choose not to assess assay performance compared to a reference method.  
2.  Measuring interval or expected values 
a)  Measuring interval 
If a biodosimetry device reports numerical (quantitative) results, submissions should include 
studies establishing the measuring interval of your device. Measuring interval studies should be 
designed to substantiate Limit of Detection (LoD), Limit of Blank (LoB), Limit of Quantitation 
(LoQ), and the linearity of the assay. The radiation absorption levels that are clinically relevant 
span from 2-10 Gy, and we recommend developing test protocols to evaluate the limitations of 
your assay outside (both above and below) the clinically relevant dose range. For instance, if 
high radiation levels can cause the assay to incorrectly report lower values (i.e., the hook effect), 
then the limitations section of the labeling should include this information. Please refer to the 
CLSI document EP6-A and EP17-A2 to assist in the design of such studies.  
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b)  Category assessments 
If a biodosimetry device reports a qualitative or semi-quantitative result, submissions should 
include studies validating the decision making cut-point(s) and how outputs will be assigned to 
clinical categories. Samples around critical decision making cut-points should be included in 
these studies.  
3.  Interference 
Interfering substances may affect assay outputs. As such, biodosimetry device submissions 
should include the results of appropriate interference studies (see CLSI Document EP07-A2) for 
quantitative tests. Please include samples near the cut-off(s) for interference testing in qualitative 
or semi-quantitative devices. Interference studies may be specific to the matrix being examined. 
For example, if the assay uses blood, interference from hemoglobin, bilirubin, and lipids should 
be examined to mimic grossly hemolytic, icteric, and lipemic samples. 
Likewise common drugs known or expected to interfere, or drugs that are expected to be 
administered in a mass radiation exposure scenario, should be tested for assay interference. For 
more information on the Federal government response plans for radiation disaster scenarios and 
a list of the applicable drugs and treatments that will be recommended in such a scenario, see the 
document entitled Planning Guidance for Response to a Nuclear Detonation.4  
In addition, you should consider how biological responses may interfere with your assay and 
develop corresponding risk mitigations necessary to provide a reasonable assurance of safety and 
effectiveness (e.g., to limit the interfering effects of the underlying biological response). This 
information may be appropriate to include in the warnings and limitations section in the labeling.  
Ultimately the decision of what compounds to test for assay interference should be based on 
applying scientific reasoning to the technological characteristics of your device and the major 
biological pathways being interrogated. For instance, if a biodosimeter incorporates expression 
profiles from a pro-inflammatory pathway, interference from anti-inflammatory drugs should be 
examined and assay effectiveness should be tested in normal volunteers with possible 
confounding diseases such as arthritis and other inflammatory conditions. Alternatively, animal 
models may be used to evaluate these potential confounders (see section IV.G.2 below).  
4.  Other analytical testing protocols  
Analytical testing protocols, in addition to those mentioned specifically above, should be 
included in biodosimetry submissions as applicable given the intended use, output, and 
technology. Reproducibility should generally be demonstrated at a minimum of three sites (of 
which at least one should be in the United States) and submissions should include an evaluation 
of sources of variability including operator-to-operator, instrument-to-instrument, day-to-day, 
                                                 
4 This document was developed by the National Security Staff Interagency Policy Coordination Subcommittee for 
Preparedness and Response to Radiological and Nuclear Threats (last accessed on April 14, 2016). Note this website 
is not controlled by FDA. 
10  Contains Nonbinding Recommendation 
and kit lot-to-kit lot variability, as applicable. Repeatability should also be evaluated. The LoD 
(limit of detection), LoQ (limit of quantification), and LoB (limit of blank), as relevant, may be 
demonstrated in a separate study for quantitative devices. The stability of kit reagents, including 
controls and calibrators, as applicable should be performed in real-time for product expiry 
dating, and should also be examined in shipping simulation studies. As noted above, studies to 
support technology-specific validation and special controls may apply, such as for molecular 
assays.  
In addition to CLSI documents indicated elsewhere in this document, the following may be 
useful in understanding how such analytical performance studies are typically designed:  
  EP05-A3, Evaluation of Precision of Quantitative Measurement Procedures 
  EP09-A3, Measurement Procedure Comparison and Bias Estimation Using Patient 
Samples  
  EP12-A2, User Protocol for Evaluation of Qualitative Test Performance 
  EP25-A, Evaluation of Stability of In Vitro Diagnostic Reagents 
E.  Controls and Calibrators 
The design of biodosimetry devices should incorporate the use of on-board or external controls 
as appropriate. Any recommended Quality Control (QC) procedures and acceptance criteria used 
during the analytical and clinical validation of your device should be included in the instructions 
for use. A description of the control material and its recommended use should be submitted along 
with the assay for premarket clearance or approval. Information that should be provided in the 
premarket submission includes control performance, value assignment, and reagent stability as 
outlined in guidance entitled Assayed and Unassayed Quality Control Material 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm0
79179.htm). Similarly, if external calibrators are required for the assay system, they should also 
be submitted with the assay. You are encouraged to consult the guidance entitled Abbreviated 
510(k) Submissions for In Vitro Diagnostic Calibrators 
(http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocu
ments/ucm092801.pdf). 
F.  Instrumentation and Software 
If your biodosimetry device utilizes software, you should submit the information listed in the 
guidances entitled Guidance for the Content of Premarket Submissions for Software Contained 
in Medical Devices 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm0
89543.htm) (Software Premarket Submissions Guidance for the duration of this document) and 
Guidance for Off-the-Shelf Software Use in Medical Devices 
(http://www.fda.gov/downloads/MedicalDevices/.../ucm073779.pdf). The information you 
should submit is determined by the level of concern, which is related to the risks associated 
with software failure, as explained in the Software Premarket Submissions Guidance referenced 
above.  
11  Contains Nonbinding Recommendation 
G.  Animal Study Considerations 
Typically, animal data are not used in IVD submissions. IVD sponsors are expected to obtain 
specimens from the intended use population either through prospective collection from a clinical 
trial or to use properly archived surplus (excess) specimens to demonstrate test performance 
when prospective studies are not feasible. However, in the case of biodosimetry devices, we 
acknowledge that appropriate human specimens may not be available. Therefore, under the 
following conditions it may be appropriate to use animal model data to supplement human 
clinical samples to demonstrate device performance:5 
  The analyte(s) being detected is not stable in archived specimens; 
  A diligent search of available specimen banks has failed to yield adequate samples for 
testing; or 
  A prospective study is either unethical, or prospective studies that may be ethically 
performed will not yield a sample set adequate to demonstrate assay performance 
over the measuring interval of the device. 
1.  Defining an appropriate animal model 
If animal model data is included in a biodosimetry device premarket submission, the submission 
should also include an appropriate justification for the animal model or models chosen. You 
should provide evidence that the model is an adequate substitute for human studies. In particular, 
establishing that there is high homology in the analyte(s) being assessed and that the animal 
model displays similar responses to radiation in the biological pathways being interrogated is 
important. We recommend that you provide a justification (e.g. appropriate published literature) that 
the radiation dose given in your selected animal model is comparable to the dose in humans.  
When choosing your animal model, you should consider transferability of experimental results, 
the influences of genetic uniformity of the model, and background knowledge of radiation 
response pathways. For example, while rodent models may be appropriate for early proof of 
concept studies in biodosimetry, their biological divergence from humans presents challenges for 
demonstrating biodosimetry effectiveness in humans. Large-animal models (non-human primate, 
canine, or porcine), in which radiation response more closely resemble those of humans, may be 
better options for effectiveness studies. You should ensure that an equal distribution of genders is 
included in pre-clinical testing and consider including animals at various age ranges so age and 
gender-related differences in radiation response can be assessed for the analytes included in the 
biodosimeter. You should carefully determine the number of animals that are required for your 
                                                 
5 Please note that this guidance refers to the use of animal model data for biodosimetry device submissions only and 
is not applicable to the Animal Rule pathway for approval of drugs (21 CFR Subpart I) or biological products (21 
CFR Subpart H). For information and recommendations on drug and biological product development under the 
Animal Rule, see FDAs final guidance Product Development Under the Animal Rule, Guidance for Industry, 
available at 
http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm399217.pdf.
 
12  Contains Nonbinding Recommendation 
studies so that meaningful data are collected. Consider consulting a biostatistician for assistance 
with determining the required animal numbers for use in these studies. 
Multiple animal models generally would not be required to demonstrate effectiveness. However, 
multiple animal models may be necessary when a single appropriate animal model cannot be 
identified for all the analytes being assessed by the device. Developers can perform a literature 
search in order to determine the most appropriate animal models to be used in biodosimetry 
device effectiveness studies.  
Please note that all device studies using animal models that are intended to support premarket 
submissions to the Agency must comply with all applicable laws and regulations including: 
  The Animal Welfare Act (7 U.S.C. 2131 et seq.) and related regulations (9 CFR Part 2) 
  The Public Health Service Policy on Humane Care and Use of Laboratory Animals6  
  Good Laboratory Practice for Nonclinical Laboratory Studies regulations (21 CFR Part 
58, including 21 CFR 58.90)7 
  U.S. Government Principles for the Utilization of and Care of Vertebrate Animals Used 
in Testing, Research, and Training8  
  Guide for the Care and Use of Laboratory Animals9  
Further, IVD sponsors should follow the guidelines below: 
  AVMA Guidelines for the Euthanasia of Animals10  
  Recognition and Alleviation of Pain in Laboratory Animals11 
2.  Effect of confounding factors on animal studies 
In some cases, animal housing environmental conditions, veterinary care, and supportive care 
might influence biodosimetry assay results. For instance, analyte expression might be influenced 
                                                 
6 U.S. Department of Health and Human Services, National Institutes of Health, Office of Laboratory Animal 
Welfare, 2015, available at http://grants.nih.gov/grants/olaw/references/PHSPolicyLabAnimals.pdf (accessed on 
April 14, 2016). 
7 While the GLP regulations do not specifically address data quality and integrity for animal studies for the accuracy 
of IVDs, FDA considers them to be a well-established and relevant system for ensuring data quality and integrity for 
animal efficacy studies. FDA recommends the use of the GLP regulations to ensure the quality and integrity of data 
from these studies. 
8 U.S. Department of Health and Human Services, National Institutes of Health, Office of Laboratory Animal 
Welfare  Public Health Service Policy on Humane Care and Use of Laboratory Animals, 2015, available at 
http://grants.nih.gov/grants/OLAW/references/phspol.htm (accessed on April 14, 2016). 
9 National Research Council of the National Academies, Committee for the Update of the Guide for the Care and 
Use of Laboratory Animals, Institute for Laboratory Animal Research, 2011, available at 
http://grants.nih.gov/grants/olaw/Guide-for-the-Care-and-Use-of-Laboratory-Animals.pdf (accessed on April 14, 
2016). 
10 American Veterinary Medical Association, AVMA Guidelines for the Euthanasia of Animals: 2013 Edition, 2013, 
https://www.avma.org/KB/Policies/Documents/euthanasia.pdf (accessed on April 14, 2016). 
11 National Research Council (US) Committee on Recognition and Alleviation of Pain in Laboratory Animals, 
Recognition and Alleviation of Pain in Laboratory Animals, 2009, Washington, DC: National Academies Press. 
13  Contains Nonbinding Recommendation 
by differences in diet or housing conditions of laboratory animals as opposed to that of the 
general human population. You should standardize husbandry of animals used in these studies, 
offering the same care, diet, and standardized enrichment to animals included in the study in 
order to help minimize experimental inconsistencies. Additionally, to avoid variability caused by 
changes in radiation response due to circadian rhythm patterns, we recommend you deliver 
radiation at the same time of day (e.g., a.m. vs. p.m.) to all animal models.  
You also may want to consider providing animal models with the same supportive care that is 
expected to be provided to people in a radiation mass exposure scenario. For example, you could 
consider providing supplemental care such as parenteral and/or oral physiologic fluids, as well as 
soft, nutrient-rich palatable foods that encourage eating. You may also consider providing 
supplemental heat, as well as antibiotics when indicated to determine if these types of medical 
interventions alter the biological responses being assessed by biodosimetry.1 For example, if 
neutrophil numbers are part of a biodosimetry algorithm, it should be understood how G-CSF 
(granulocyte-colony stimulating factor) treatments affect resulting radiation level estimations. 
This information may be critical to ensuring appropriate labeling is provided for the assay. 
3.  Bridging animal data to human data  
When animal model data are necessary to demonstrate the effectiveness of a biodosimeter at a 
clinically relevant dose or dose range, two types of studies should be provided. First, a set of 
experiments should be designed to bridge between the animal results and the available human 
clinical information. For instance, if available human studies were derived from whole body 
exposure delivered in 2 Gy fractions over the course of a week, then an animal study should be 
performed to mirror this exposure pattern to demonstrate how animal results and human data are 
reflective of each other. There are several complex statistical considerations in determining the 
species comparison that will be appropriate depending on device output (e.g., quantitative, 
qualitative, or semi-quantitative) and technology type. These statistical methodologies should be 
discussed with the Agency on a case-by-case basis. Once an appropriate demonstration has 
shown that animal results can be bridged to human clinical experience, then studies in animals 
may be performed to address device performance at radiation doses, dose rates, time courses, and 
sources that cannot be examined in human clinical studies.  
Animal studies may also be used to address situations that may not be easily addressed using 
human clinical studies. Depending on the mechanism of action or biological pathways assessed 
by the device, you could consider testing animal models or providing literature support for the 
impact that commonly used drugs such as anti-cholesterol, anti-hypertension, diabetic drugs, and 
other common drugs may have on the analyte (e.g., a particular metabolic pathway, CYP450, or 
growth factors) being evaluated. Other situations that may confound assay results such as 
combined injury can also be examined in animal models.  
Animal studies should be designed to use the fewest possible animals to demonstrate statistical 
significance in accordance with Institutional Animal Care and Use Committee (IACUC) 
guidelines.12  Because these studies will be critical to demonstrate the performance of a 
                                                 
12 See note 8. 
14  Contains Nonbinding Recommendation 
biodosimeter and because alternative study designs will be needed to minimize animal numbers, 
it is encouraged that study protocols be submitted to us prior to the onset of testing in order to 
gain Agency concurrence on study parameters and statistical analysis plans. Please refer to the 
appendix in section V of this document for more information on statistical considerations for 
biodosimetry study designs. 
H.  Establishing Performance Characteristics  Clinical 
Validation Studies with Human Samples 
FDA expects that biodosimeter safety and effectiveness will be established in an appropriately 
designed clinical study that captures the device performance in human subjects. However as 
discussed above, non-clinical models may be used to supplement human clinical data by 
providing information on doses, dose rates, time-courses and radiation sources that cannot be 
ethically obtained in a human clinical study. These pivotal studies should be designed with an 
appropriate statistical analysis plan in place with pre-defined acceptance criteria. You are 
encouraged to refer to Appendix A in section V of this document and the guidance entitled 
Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm0
71148.htm) for more information. 
1.  Prospective clinical studies 
Studies may be performed to prospectively assess biodosimeter performance in the context of 
radiation delivered for therapeutic purposes. Because patient numbers may be limited, you 
should consider a study design that collects multiple samples from each patient over the course 
of their therapy. Additionally, whenever possible you should collect specimens prior to radiation 
and/or other therapy as a clinical baseline. Ideally, you should design studies to assess 
biodosimeter performance beyond the range of times post-exposure that you intend to claim in 
labeling. For example, if you intend your device to have labeling claims from 24 hours to 7 days 
post-exposure, your studies should include a minimum of 8 days post-exposure. However, we 
acknowledge that therapies administered pre-, post-, or concurrently with radiation in some 
patient populations may confound biodosimetry test results. As such, lack of human clinical data 
across the intended times post-exposure may be acceptable with an appropriate scientific 
justification and accompanying supplemental animal data. 
For all prospective clinical studies, information should be captured on the dose, dose rate, time-
course and radiation source for all patients. In addition, basic demographic information should be 
collected, including patient age, gender, and race. Information on the patients clinical condition 
(e.g., body mass index and medications) should also be collected whenever possible.  
Prospective IVD clinical studies that do not require an invasive sampling procedure and for 
which the test results are not used to support patient management are generally considered to 
meet the requirements under 21 CFR 812.2(c)(3) to be exempt from the Investigational Device 
Exemption requirements in part 812 with the exception of 21 CFR section 812.119. If, however, 
you have concerns about the risk classification of your prospective clinical study, you should 
submit a risk-determination pre-submission as outlined in the Requests for Feedback on 
15  Contains Nonbinding Recommendation 
Medical Device Submissions: The Pre-Submission Program and Meetings with Food and Drug 
Administration Staff 
(http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocu
ments/UCM311176.pdf). 
We acknowledge that there are a number of challenges associated with prospectively collecting 
adequate clinical samples for device effectiveness testing. Therefore, it is recommended that you 
use the pre-submission process to discuss clinical study design and implementation prior to the 
onset of testing. Pre-submissions should ideally include the clinical validation study protocol, 
statistical analysis plan, and a description of sample acquisition strategies. 
2.  Clinical studies using banked samples 
If the analyte or analytes being assessed by a biodosimetry device are suitably stable in the 
relevant test matrix, then you are encouraged to utilize appropriately banked samples (collected 
for other purposes) to demonstrate the clinical performance of your biodosimetry device. For 
more information, please refer to the guidance entitled Informed Consent for In Vitro 
Diagnostic Device Studies Using Leftover Human Specimens that are Not Individually 
Identifiable 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm0
78384.htm). Appropriate retrospective samples should have documentation on basic patient 
demographics (but not personally identifiable information) and basic information on the 
radiation exposure profile. 
 
3.  Limitations of clinical studies 
We acknowledge that there may be significant limitations in the interpretation of clinical studies 
for biodosimetry submissions. For instance, therapeutic dose rates will not be reflective of the 
expected dose rates that would be experienced by someone in a radiological disaster. 
Additionally, clinical studies may not be reflective of all possible types of radiological disasters 
for which the biodosimeter is intended. Thus, in addition to the animal studies and analytical 
studies discussed above, biodosimetry submissions should include a discussion of the limitations 
of the clinical study, and how analytical studies, animal studies, or a combination of analytical 
and animal studies have been used to mitigate these limitations in order to demonstrate a 
reasonable assurance of the safety and effectiveness of the device for its intended use. 
I.  Labeling 
The labeling of a biodosimetry device includes the instructions for use, package inserts, and any 
outer box or container labels for the device itself, reagents, and control materials, as applicable. 
The following references will be useful in developing clear and complete labeling for your 
device.  
  The guidance entitled Guidance on Medical Device Patient Labeling 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocument
s/ucm070782.htm) 
  CLSI document GP-14 Labeling of Home-Use In Vitro Testing Products 
16  Contains Nonbinding Recommendation 
  In Vitro Diagnostic Device Labeling Requirements 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/Overview/DeviceL
abeling/InVitroDiagnosticDeviceLabelingRequirements/default.htm) 
In addition to the references above and the information already provided in this guidance 
document, the following should be considered when developing labeling for biodosimetry 
devices that complies with the labeling requirements outlined in 21 CFR Parts 801 (for general 
labeling requirements) and 809 (for additional IVD labeling requirements), as applicable. 
1.  Instructions for use 
For biodosimeters intended for use by lay persons, information required by 21 CFR 809.10(b) 
should be described in a manner that lay users can understand. Detailed technical information 
(e.g., scientific principles of test procedure or statistical analysis of data) may be presented in a 
separate section followed by clarifying statements appropriate for lay users. The following 
should also be taken into account when drafting appropriate instructions for use for radiation 
biodosimetry devices. 
  The labeling must provide instructions for specimen collection and preparation (see 21 
CFR 809.10(b)(7)). Instructions should be drafted with the intended end user in mind. For 
example, consider whether a trained healthcare provider will be collecting the sample or 
if the patient will be instructed to do so. 
  The labeling must provide a step-by-step outline of recommended procedures and 
operating instructions for the instrument (see 21 CFR 809.10(b)(8) and 21 CFR 
809.10(b)(6)(v)). Ideally, numbering rather than bullet points should be used for clarity. 
  Labeling must describe details of calibration and of quality control procedures (see 21 
CFR 809.10(b)(8)(v) and 21 CFR 809.10(b)(8)(vi)). These instructions are to help ensure 
optimal performance of the system. This section should include recommendations for 
how and when to perform quality control checks and instructions for what to do if the 
control material values are not within the allowable ranges.  
2.  Limitations 
Labeling must include a statement of limitations of the procedure, including known extrinsic 
factors or interfering substances affecting results (see 21 CFR 809.10(b)(10)). You should also 
include testing conditions that may cause clinically significant errors due to bias or imprecision 
(e.g., combined injury, high dose rates, or alternative sources of radiation). You should also note 
all known contraindications. This section should thoroughly describe the situations of use that 
were not examined in performance testing of the device. For instance, if device performance was 
not assessed using neutron radiation sources, then this information should be included in the 
labeling. 
17  Contains Nonbinding Recommendation 
3.  Interpretation of results 
Labeling must include a description of the expected values for your device (qualitative) or 
measuring interval (quantitative) (see 21 CFR 809.10(b)(11)). In the case of a qualitative device, 
we recommend that the expected values be portrayed in terms of expected values for non-
irradiated subjects, and around the clinical decision making cut-points that were evaluated for 
your device (e.g., 2 Gy and/or 10 Gy). If the results are qualitative, you should explain how to 
interpret positive and negative results, including their clinical significance. Alternatively, if the 
device is quantitative, you should explain the clinical significance of the outputs along the 
measuring interval. A statement should be included to interpret results in the context of other 
clinical signs and symptoms as well as any known dosimetric or radiation dispersal data 
associated with the patients location. 
You should also provide directions for the interpretation of the results of controls (performance 
monitors) and provide a statement that if controls do not perform as expected, assay results are 
invalid. Instructions should also be provided for any situation in which the end user should repeat 
a test. 
4.  Performance characteristics 
Labeling must include specific performance characteristics of the device (see 21 CFR 
809.10(b)(12)). All studies, including bench testing, animal testing, and clinical studies should be 
summarized in the package insert of the assay. Performance data should be presented clearly and 
accurately, ideally in both graphical and text formats. Clinical information that was obtained 
through animal studies alone (with no human clinical supporting data) should be specifically 
highlighted with a disclaimer that the performance of the assay has not been evaluated in clinical 
samples under these specific conditions.  
J.  CLIA Categorization 
The CLIA (42 U.S.C. 263a) regulates laboratory testing and requires that clinical laboratories 
obtain a certificate before accepting materials derived from the human body for the purpose of 
providing information for the diagnosis, prevention, or treatment of any disease or the 
impairment of, or assessment of the health of human beings. The type of CLIA certificate a 
laboratory obtains depends upon the complexity of the tests it performs. CLIA regulations 
describe the following three levels of test complexity: waived tests, moderate complexity tests, 
and high complexity tests, 42 CFR 493.5(a). 
CDRH determines the CLIA categorization of IVD tests at the time of review of a premarket 
notification submission (510(k)) or a premarket approval application (PMA).  Biodosimeters 
intended for field triage that are cleared for home use for the purposes of CLIA are CLIA waived 
(42 CFR 493.15).  In addition, a biodosimeter meets the statutory criteria for CLIA waiver if the 
sponsor demonstrates in studies conducted at the intended use site that the biodosimeter employs 
methodologies that are so simple and accurate as to render the likelihood of erroneous results 
negligible or that the biodosimeter poses no reasonable risk of harm to the patient if the test is 
performed incorrectly. If a sponsor of a test categorized as moderate complexity believes their 
18  Contains Nonbinding Recommendation 
test meets the statutory criteria for CLIA waiver, they may submit a CLIA Waiver by 
Application to request categorization of the test system as waived. A Dual 510(k) and CLIA 
Waiver by Application pathway is also available. For additional information regarding CLIA 
Categorizations and CLIA Waivers by Application including the Dual 510(k) and CLIA Waiver 
by Application pathway, refer to the guidance entitled Administrative Procedures for CLIA 
Categorization 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm0
70762.htm).   
19  Contains Nonbinding Recommendation 
V.  Appendix A: Statistical Considerations for 
Biodosimetry Devices 
This appendix includes some statistical considerations for biodosimetry devices. Further 
statistical considerations for diagnostic devices that may be applicable to biodosimetry devices 
are comprehensively discussed in the guidance entitled Statistical Guidance on Reporting 
Results from Studies Evaluating Diagnostic Tests 
(http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm0
71148.htm) and further concepts and principles related to designing medical device studies are 
discussed in the guidance entitled Design Considerations for Pivotal Clinical Investigations for 
Medical Devices 
(http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocu
ments/UCM373766.pdf). In addition, as discussed above, it is recommended that you use the 
pre-submission process to discuss statistical analysis plans and methods before initiating any data 
collection for your clinical, pre-clinical, and analytical studies. 
A.  Study Blinding (Masking)  
To avoid bias in the device result, the user of the biodosimetry device should be unaware of (i.e., 
blinded to) the actual level of radiation absorption (e.g., the administered dose). Likewise, to 
avoid bias in the reference method measurement, the user of the comparative method should be 
unaware of the device result. In general, the user should be unaware of any results from other 
diagnostic evaluations, and vice versa.  
B.   Confidence Intervals for Estimates of Performance  
Confidence intervals for performance estimates are also referred to as precision of estimation in 
clinical study literature. Performance variability is controlled by the sample size of the study and 
is another key consideration when evaluating a study design and study results. With a larger 
sample size, an estimate of performance is subject to less sampling variability. Uncertainty of the 
estimation is thus reduced. The estimate becomes less imprecise, leading to a narrower 
confidence interval of likely values for the true performance. If you have more than one study, 
the precision of estimation might be increased by a careful pre-planned analysis of combined 
studies, if appropriate.  
C.   Study Analysis 
The protocol for a biodosimeter study should include a Statistical Analysis Plan (SAP). The SAP 
is used to interpret the study data in support of the safety and effectiveness of the device for its 
intended use. The SAP should be pre-specified and provided in enough detail to permit FDA 
review. The analysis plan should define the performance measures (e.g., specificity, sensitivity) 
to be evaluated. Any success criteria on the performance measures should be clearly pre-defined 
using descriptive and mathematical statements (e.g., the null and alternative hypotheses for a 
significance test, the estimator for a performance measure, the method for deriving a confidence 
interval, etc.). Unplanned post-hoc analyses are discouraged as primary evidence of safety and 
20  Contains Nonbinding Recommendation 
effectiveness. Post-hoc analyses can inflate the study-wise type I error rate (probability of false 
statistical significance) and therefore are generally considered exploratory rather than 
confirmatory evidence of safety and effectiveness. 
In particular, the SAP should describe how sample size for the study was determined. Sample 
size determination should be consistent with the pre-planned statistical analyses of the study, 
especially the primary analyses. Assumptions underlying the statistical power of the study to 
demonstrate a performance claim should be provided in detail. 
In clinical studies of IVDs, laboratories often produce results that are not described by a defined 
category or numerical value. The terms used to describe these results include uninterpretable, 
invalid, and indeterminate. Your test results may also be called equivocal. For various 
reasons including inability to obtain new samples after assay failure, results may be missing.  
The SAP should include a plan for dealing with all these possible device results and samples or 
specimens that are unavailable or unevaluable. For example, the plan may include reporting the 
number and proportion of subjects without a valid device result by the reason a valid result was 
not obtained. If repeated application (after the first reading) of the device on a subject or 
specimen is not intended, is not possible, or would not be helpful (e.g., the device result will 
always be uninterpretable), then uninterpretable device results, for example, could be treated as a 
separate category for the purpose of analysis. If repeat measurement of subjects or specimens is 
possible and appropriate, then imputation of missing device results can sometimes aid the 
statistical analysis and interpretation of study data. Further comments with respect to missing 
data are in section V.C.4 below.  
If the design of your study is adaptive, its adaptive features should be pre-planned (i.e., the study 
should be adaptive by design). Before conducting an adaptively designed study, its operating 
characteristics (e.g., type 1 error rate, power) and its potential for introducing operational bias 
into the study (due to the adaptive features) should be evaluated. Some examples of adaptations 
based on interim analysis include stopping the study early (e.g., for futility, re-estimating sample 
size, and changing a hypothesis). Monitoring a study until it has a pre-defined number of 
subjects with or without a condition is an adaptive design feature which does not ordinarily 
require special consideration. The literature on adaptive design for diagnostic studies is 
unfortunately scant, but some references are available.13  
Corresponding two-sided 95% confidence intervals should be provided. The method used to 
estimate these measures and their corresponding 95% confidence intervals should be clearly pre-
specified. If multiple measurements are obtained per subject, the statistical analysis (e.g., 95% 
confidence interval) should account for the correlation structure of the within-subject 
measurements using a valid statistical method. 
                                                 
13 Gerke O, Hoilund-Carlsen PF, Poulsen MH, Vach W. Interim analyses in diagnostic versus treatment studies: 
Differences and similarities. Am J Nucl Med Mol Imaging 2012; 2(3): 34452; Tang LL, and Liu A. Sample size 
recalculation in sequential diagnostic trials, Biostatistics 2010; 11(1): 151-163. 
21  Contains Nonbinding Recommendation 
1.  Quantitative, continuous, or semi-quantitative output 
Statistical analyses and methods depend on the type of results provided by the biodosimetry 
device. For devices providing a quantitative measurement, the bias and imprecision of the 
measurement should be evaluated along with other performance characteristics. For example, 
difference plot methodology can be used to compare the level of radiation absorption predicted 
by the device with the actual level delivered (actual dose or reference level) over the measuring 
interval.14 Comparison of device output and delivered radiation dose results near clinical 
decision making cut-points is essential. The systematic difference (bias) of the device result 
should be estimated near these decision points. Please refer to the CLSI document EP09-A3, 
Measurement Procedure Comparison and Bias Estimation Using Patient Samples; Approved 
Guideline--Third Edition, and EP05-A3, Evaluation of Precision of Quantitative Measurement 
Procedures; Approved Guideline--Third Edition.  
Receiver Operating Characteristic (ROC) analysis might also be useful to evaluate the diagnostic 
accuracy of a biodosimetry device reporting a quantitative, continuous, or semi-quantitative 
result, or reporting a qualitative result derived from an underlying value that is quantitative, 
continuous, or semi-quantitative. ROC analysis evaluates the overall ability of the device to 
discriminate between subjects with and without a condition of interest. On an ROC plot, the false 
positive and true positive fractions (1  specificity, sensitivity) are plotted for each possible cut-
off in the value as it is varied across the entire range of observed values, resulting in an ROC 
curve. An advantage of ROC analysis can be that the plot will display the estimated sensitivity 
and specificity of the device throughout a range of clinical decision making cut-points. The area 
under the ROC curve (AUC) is a global measure of device discrimination performance, with 
AUC values of 0.5 and 1.0 indicating random and perfect discrimination, respectively. Please 
refer to the CLSI document EP24-A2, Assessment of the Diagnostic Accuracy of Laboratory 
Tests Using Receiver Operating Characteristic Curves; Approved Guideline--Second Edition, as 
well as the following references.15 If the condition of interest is not binary (e.g., dose or level of 
radiation absorption), generalizations of AUC exist for evaluating the discrimination ability of 
medical tests.16  
2.  Qualitative output 
For devices that are designed to provide a qualitative output around a clinical decision making 
cut-point, device evaluation should include its performance metrics using measures such as 
                                                 
14 Bland JM, and Altman DG. Measuring agreement in method comparison studies. Statistical Methods in Medical 
Research 1999; 8(2):13560. 
15 Pepe M, The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford, 2003; Zhou XH, 
Obuchowski NA, and McClish DK, Statistical Methods in Diagnostic Medicine Bias, 2nd ed, Wiley, 2011; Zou K, 
Statistical Evaluation of Diagnostic Performance  Topics in ROC Analysis, Chapman & Hall/CRC, 2012; 
Krzanowski WJ, and Hand DJ. ROC Curves for Continuous Data, Chapman & Hall/CRC, 2009; Zweig MH, and 
Campbell G. Receiver-Operating Characteristic (ROC) Plots: A Fundamental Evaluation Tool in Clinical Medicine, 
Clin Chem 1993; 39(4):561-577.  
16 Obuchowski NA. An ROC-type measure of diagnostic accuracy when the gold standard is continuous-scale. Stat 
Med 2006; 25:481-493; Obuchowski NA. Estimating and comparing diagnostic tests accuracy when the gold 
standard is not binary, Acad Radiol 2005; 12:11981204. 
22  Contains Nonbinding Recommendation 
sensitivity, specificity, and the negative and positive diagnostic likelihood ratios (NLR, PLR). 
PLR is defined as sensitivity / (1  specificity), the ratio of the true positive fraction to the false 
positive fraction. NLR is defined as (1  sensitivity) / specificity, the ratio of the false negative 
fraction to the true negative fraction. Larger values of PLR and smaller values of NLR indicate 
better classification of the condition status. PLR and NLR are proportional to the odds that test 
positive and negative subjects have a condition, respectively.  
3.  Analytical imprecision 
Imprecision describes the extent of disagreement or variability of a set of replicate 
measurements. For biodosimetry devices providing a quantitative or continuous value (e.g., 
radiation dose or level), the standard deviation and coefficient of variation are common measures 
of imprecision. Repeatability is the imprecision when repeated measurements are taken under the 
same conditions of measurement. Intermediate imprecision is the imprecision when the repeated 
measurements are taken with some conditions intentionally varied (e.g., run, day, operator, 
instrument, reagent lot). See CLSI document EP05-A3, Evaluation of Precision of Quantitative 
Measurement ProceduresThird Edition. 
For biodosimetry devices intended to report qualitative results, the percent agreement of the 
replicate device results with the qualitative result that is expected for the subject or specimen 
may be reported.  
4.  Missing data 
The SAP should describe how missing data will be handled and documented. Reported results 
can be misleading if subjects with missing measurement results are excluded from the analysis, 
the report, or both the analysis and the report. All subjects for whom a measurement was 
attempted need to be accounted for when reporting results. It is important to analyze the impact 
of missing data on the conclusions obtained from the study. In some cases it may be necessary to 
assess if study conclusions are robust given the missing data, and in such cases an intent-to-
diagnose or ITD analysis can be performed. An ITD analysis includes every subject or specimen, 
regardless of whether the subject or specimen is missing the radiation biodosimetry device result, 
the actual dose, the clinical reference diagnosis, or other results from comparators.  
D.  Feature Selection During Algorithm Development 
If your biodosimetry device incorporates multiple pieces of information into an algorithm in 
order to produce a single output, validation of this algorithm will be important to understand the 
performance capabilities of the assay. During algorithm development, it is generally important to 
obtain a trustworthy estimate of the algorithms performance before the pivotal performance 
validation study.  
Cross-validation is a procedure for estimating the performance of an algorithm on the same 
dataset on which it was developed. The developmental dataset is split repeatedly into training 
and test datasets, with the algorithm developed on the training set and evaluated on the test set. 
The performance estimates obtained for the many splits are then averaged. In bootstrap cross-
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validation, a training set of the same size as the original dataset is obtained by sampling the 
subjects or specimens with replacement and evaluated on the remaining unselected subjects or 
specimens.17  
Cross-validation requires that algorithm development be automated so may not be possible if the 
algorithm development process has subjective aspects. All steps of the algorithm construction 
process, including and especially the step of selecting the features (analytes, measurands, etc.) to 
be used by the algorithm, should be cross-validated, otherwise the performance estimate will 
likely be biased.18 Please note that internal cross-validation is not a substitute for pivotal 
validation in a dataset that is independent of (external to) the datasets used for development. 
Further, for the pivotal validation, the final version of the test should be used. 
E.   Electronic Data  
You are encouraged to provide an electronic version of the line data with your submission in an 
appropriate format such that the datasets are well-described and interpretable. These and the 
associated programs used to generate your results should be included in a format which can be 
easily transferred into statistical software. The information at the following URL may be helpful 
as you prepare these materials:  
http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/HowtoMarketYourDevice/P
remarketSubmissions/ucm136377.htm 
                                                 
17 Efron B, and Tibshirani R. Improvements on Cross-Validation: The .632+ Bootstrap Method. J Amer Statist Assoc 
1997; 92(438):548-560.  
18 Simon R et al, Pitfalls in the Use of DNA Microarray Data for Diagnostic and Prognostic Classification, J 
National Cancer Institute 2003; 95(1):14-18. 
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