Biomarker 
Qualification: 
Evidentiary Framework 
Guidance for Industry and FDA 
Staff 
 
 
DRAFT GUIDANCE
 
 
  This guidance document is being distributed for comment purposes only. 
 
Comments and suggestions regarding this draft document should be submitted within 60 days of 
publication in the Federal Register of the notice announcing the availability of the draft 
guidance.  Submit electronic comments to https://www.regulations.gov.  Submit written 
comments to the Dockets Management Staff (HFA-305), Food and Drug Administration, 5630 
Fishers Lane, Rm. 1061, Rockville, MD  20852.  All comments should be identified with the 
docket number listed in the notice of availability that publishes in the Federal Register. 
 
For questions regarding this draft document, contact (CDER) Office of New Drugs at CDER-
BiomarkerQualificationProgram@fda.hhs.gov , or (CBER) Office of Communication, Outreach 
and Development, 800-835-4709 or 240-402-8010.   
 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
Center for Biologics Evaluation and Research (CBER) 
 
December 2018 
Drug Development Tools 
 
 
   
Biomarker 
Qualification: 
Evidentiary Framework 
Guidance for Industry and FDA 
Staff 
 
 
 
Additional copies are available from: 
Office of Communications, Division of Drug Information  
Center for Drug Evaluation and Research 
Food and Drug Administration 
10001 New Hampshire Ave., Hillandale Bldg., 4th Floor  
Silver Spring, MD 20993-0002  
Phone: 855-543-3784 or 301-796-3400; Fax: 301-431-6353 
Email: druginfo@fda.hhs.gov  
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm 
and/or 
Office of Communication, Outreach, and Development  
Center for Biologics Evaluation and Research 
Food and Drug Administration 
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Silver Spring, MD  20993-0002 
Phone: 800-835-4709 or 240-402-8010; Email: ocod@fda.hhs.gov 
https://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guidances/default.htm 
 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
Center for Biologics Evaluation and Research (CBER) 
 
December 2018 
Drug Development Tools 
  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
TABLE OF CONTENTS 
 
 
 
I.  INTRODUCTION............................................................................................................. 1 
II.  BACKGROUND ............................................................................................................... 3 
III.  EVIDENTIARY FRAMEWORK ................................................................................... 3 
A.  Needs Assessment ........................................................................................................................... 4 
B.  Context of Use ................................................................................................................................ 4 
C.  Assessment of Benefits and Risks ................................................................................................. 6 
D.  Determining Evidence That Is Scientifically Sufficient To Support COU ............................... 7 
IV.  ANALYTICAL CONSIDERATIONS ............................................................................ 9 
V.   STATISTICAL CONSIDERATIONS .......................................................................... 10 
  Contains Nonbinding Recommendations 
Draft  Not for Implementation 
Biomarker Qualification: Evidentiary Framework 
1 
1
Guidance for Industry and FDA Staff  
2 
3   
4   
5  This draft guidance, when finalized, will represent the current thinking of the Food and Drug 
6  Administration (FDA or Agency) on this topic.  It does not establish any rights for any person and is not 
7  binding on FDA or the public.  You can use an alternative approach if it satisfies the requirements of the 
8  applicable statutes and regulations.  To discuss an alternative approach, contact the FDA staff responsible 
9  for this guidance as listed on the title page.   
10   
11   
12   
13  I.  INTRODUCTION  
14   
15  This guidance for biomarker2 development stakeholders and Food and Drug Administration 
16  (FDA) staff provides recommendations on general considerations to address when developing a 
17  biomarker for qualification under the 21st Century Cures Act (Cures Act) that added new section 
18  507of the Federal Food, Drug, and Cosmetic Act (FD&C Act), Qualification of Drug 
19  Development Tools.3  This guidance discusses the evidentiary framework that should be used to 
20  support biomarker qualification, as that term is now used in section 507 of the FD&C Act, and it 
21  was informed by public workshops that predated the Cures Act.   
22   
23  The evidentiary framework described in this guidance identifies the recommended components 
24  of a biomarker development program, including determining the type and level of evidence 
25  sufficient to support qualification, and addresses how these components interrelate to inform the 
26  evidentiary framework.  This evidentiary framework is broadly applicable to all biomarker 
27  qualification submissions, regardless of the type of biomarker or context of use (COU).  
28  Qualification of a biomarker is a determination that within the stated COU, the biomarker can be 
29  relied on to have a specific interpretation and application in drug development and regulatory 
30  review.4  Thus, a qualified biomarker can be used across multiple drug5 development programs 
31  under the COU for which it was qualified.  Requests for qualification of a biomarker should 
32  address the evidentiary framework discussed in this document.  
33   
                                                 
1 This guidance has been prepared by the Office of New Drugs and the Office of Translational Sciences in the 
Center for Drug Evaluation and Research (CDER) at the Food and Drug Administration in cooperation with the 
Center for Biologics Evaluation and Research (CBER). 
2 Throughout this guidance, the term biomarker is intended to include both single entity and composite biomarkers 
(biomarkers consisting of several individual biomarkers whose measurements are combined in a defined algorithm 
to reach a single interpretive output).  References in this guidance to the use of a biomarker in drug development 
imply making a decision in drug development based upon the measurement of the biomarker.   
3 Section 507 of the FD&C Act (21 U.S.C. 357) was added by section 3011(a) of the Cures Act (Public Law 114-
255). 
4 FD&C Act section 507(e)(7). 
5 For the purposes of this guidance, all references to drugs or drug products include both human drugs and biological 
drug products regulated by CDER and CBER, unless otherwise specified. 
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34  Many principles discussed in this guidance could also be appropriate when considering the 
35  evidence scientifically sufficient to support the use of a biomarker in an individual drug 
36  development program (e.g., investigational new drug application, new drug application, or 
37  biologics license application submissions).  The specifications for medical devices and the 
38  processes and evidence that support obtaining marketing authorization for medical devices, 
39  including the qualification of a biomarker for use in the investigation of a medical device or use 
40  with a medical device, are outside the scope of this document.   
41   
42  This guidance was informed by several public workshops6 that discussed the science to support 
43  biomarker qualification; these workshops convened before the enactment of the Cures Act.  
44  Development of this guidance was also greatly facilitated by the efforts from the biomarker 
45  development communityincluding FDA, National Institutes of Health (NIH), industry, 
46  academia, patient groups, and the nonprofit sectorthat developed an October 2016 white paper 
47  describing a Framework for Defining Evidentiary Criteria for Biomarker Qualification.7  In 
48  addition to considering public comments received regarding this guidance, FDA anticipates that 
49  the Agency will incorporate additional information required under the Cures Act and discussed 
50  in the reauthorized Prescription Drug User Fee Act (PDUFA VI) goals letter (PDUFA VI goals 
51  letter)8 in a subsequent revised draft version of this guidance.  Ultimately, FDA anticipates that a 
52  future revised draft guidance on this topic will meet the statutory requirement for draft guidance 
53  on a conceptual framework describing appropriate standards and scientific approaches to 
54  support the development of biomarkers described in section 3011(b)(1)(A) of the Cures Act and 
55  meet the commitment in section (1)(J)(6)(d) of the PDUFA VI goals letter related to publishing a 
56  draft guidance on general evidentiary standards for biomarker qualification.  As part of FDAs 
57  efforts to delineate the conceptual framework to support biomarker qualification and the general 
58  evidentiary standards for biomarker qualification, FDA also anticipates that subsequent guidance 
59  on biomarker qualification will address specific aspects of evidentiary considerations (e.g., 
60  statistical, analytical) in greater detail. 
61   
62  In general, FDAs guidance documents do not establish legally enforceable responsibilities.  
63  Instead, guidances describe the Agencys current thinking on a topic and should be viewed only 
64  as recommendations, unless specific regulatory or statutory requirements are cited.  The use of 
65  the word should in Agency guidances means that something is suggested or recommended, but 
66  not required.  
                                                 
6 Workshops convened to discuss the science to support biomarker qualification included:  Institute of Medicine 
Workshop on Biomarker Qualification (2009), FDA co-sponsored Biomarkers Workshop with Howard Hughes 
Medical Institute (2013), FDA co-sponsored Brookings meeting on Advancing the Use of Biomarkers and 
Pharmacogenomics (2014), FDA co-sponsored workshop with M-CERSI and the Critical Path Institute on 
Evidentiary Considerations for Integration of Biomarkers in Drug Development (2015), NIH-FDA Workshop on 
Biomarker Glossary of Terms (2015), the National Biomarker Development Alliances Workshop on 
Collaboratively Building a Foundation for FDA Biomarker Qualification (2015), and Foundation for the NIH-FDA 
Workshop on Developing an Evidentiary Criteria Framework for Safety Biomarkers Qualification (2016). 
7 Biomarkers Consortium Evidentiary Standards Writing Group:  Framework for Defining Evidentiary Criteria for 
Biomarker Qualification.  Final version 10/20/2016.  Available at:   
https://fnih.org/sites/default/files/final/pdf/Evidentiary%20Criteria%20Framework%20Final%20Version%20Oct%2
020%202016.pdf. 
8 The PDUFA VI goals letter is available at: 
https://www.fda.gov/downloads/ForIndustry/UserFees/PrescriptionDrugUserFee/UCM511438.pdf. 
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67   
68  II.  BACKGROUND  
69   
70  Historically, biomarkers gained acceptance for use in drug development after evidence from 
71  scientific and medical communities accumulated over time, leading to the recognition of the role 
72  and value of the biomarker in decision-making.  This evidence was considered as part of drug-
73  specific development efforts, and there was no formal regulatory process to assess the broader 
74  utility of the biomarker independent from its use in a specific drug program.  Even after the 
75  Center for Drug Evaluation and Research established the legacy (pre-Cures Act) Biomarker 
76  Qualification Program in 2007, progress in biomarker development has been hampered by the 
77  lack of a clear, predictable, and specific regulatory framework for the type and level of evidence 
78  sufficient to support regulatory decision-making using biomarkers.  This guidance is an 
79  additional step towards informing future guidances that will specifically address this need, the 
80  Cures Act requirements, and PDUFA commitments.  Throughout this guidance, FDA uses 
81  certain terms that appear in the FDA-NIH Biomarker Working Group, BEST (Biomarkers, 
82  EndpointS, and other Tools) Resource.  The BEST Resource includes a taxonomy of terms that 
83  can be accessed online and on which FDA welcomes comment.9   
84   
85  III.  EVIDENTIARY FRAMEWORK  
86   
87  For a biomarker development effort to be successful, the biomarker should be clearly identified 
88  and characterized, including its source material or matrix and its method of measurement.  The 
89  biomarker should be clearly identified based on the specific analyte (e.g., fibrinogen), anatomic 
90  feature (e.g., joint angle), or physiological characteristic (e.g., blood pressure) being measured.  
91  For composite biomarkers, it is important to list the individual biomarker components and how 
92  these components are interrelated (e.g., a description of an algorithm or scoring system).  If 
93  individual components have differential weighting, the description should include the biologic 
94  rationale to support this decision.  Because biomarkers are measured entities, it is important to 
95  describe the biomarker source or material for measurement, which determines the biomarker 
96  type (e.g., molecular, histologic, radiographic, physiologic characteristic).  For example, a 
97  molecular biomarker obtained from a biofluid should state the sample matrix (e.g., plasma, 
98  urine), and a radiographic biomarker should include the organ or tissue imaged (e.g., kidney).  
99  For radiographic biomarkers, it may be appropriate to include the assay/imaging modality or 
100  method for interpretation (e.g., dual-energy x-ray absorptiometry, T4/T1 ratio by 
101  acceleromyography). 
102   
103  The evidentiary framework that should be considered when determining the type and level of 
104  evidence sufficient to support qualification of a biomarker consists of several components.  The 
105  framework includes:  (1) describing the drug development need, (2) defining the COU, (3) 
106  considering potential benefits if the biomarker is qualified for use, and (4) considering potential 
                                                 
9 The FDA-NIH Biomarker Working Group BEST Resource is available at 
https://www.ncbi.nlm.nih.gov/books/NBK326791/.  The BEST Resource contains a glossary intended to harmonize 
terms used in translational science and medical product development, with a focus on terms related to study 
endpoints and biomarkers.  The glossary will be periodically updated with additional terms and clarifying 
information (last accessed March 1, 2018).  
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107  risks associated with the proposed use of the biomarker in a drug development program (see 
108  Figure 1).  
109   
110  Figure 1:  Evidentiary Framework 
111   
112   
113   
114  A.  Needs Assessment 
115   
116  The needs assessment describes why a biomarker is needed for drug development, including how 
117  its use might promote drug development in areas where there is an unmet medical need.  The 
118  needs assessment should describe the current drug development landscape, such as the use and 
119  limitations of available biomarkers or other drug development tools, and the added value the 
120  novel biomarker could provide to the current drug development process.  The needs assessment 
121  should also consider the degree to which there is an unmet medical need in the relevant condition 
122  or conditions (e.g., a greater unmet need if there is a serious condition with no or limited 
123  treatment) that can be more efficiently or effectively addressed through use of the proposed 
124  biomarker in drug development.  The needs assessment can include factors that FDA may 
125  determine to be helpful for informing FDA prioritization of the review of full qualification 
126  packages, including, as applicable, the severity, rarity, or prevalence of the disease or condition 
127  targeted by the biomarker; the availability or lack of alternative treatments for such disease or 
128  condition; and the identification (by FDA or by biomedical research consortia and other expert 
129  stakeholders) of a biomarker and its proposed COU as a public health priority.10 
130   
131  B.  Context of Use  
132   
133  According to section 507(e)(4) of the FD&C Act, the term context of use means, with respect 
134  to a drug development tool, the circumstances under which the drug development tool is to be 
135  used in drug development and regulatory review.  The COU is a concise description of a 
136  biomarkers specified use in drug development.  The COU includes two components:  (1) the 
                                                 
10 FD&C Act section 507(a)(2)(C). 
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137  biomarker category and (2) the biomarkers proposed use in drug development.  Each biomarker 
138  qualification effort should identify a single COU.  
139   
140  Biomarkers can be disease-related or treatment-related and should be classified by the BEST 
141  biomarker category, selected from the following (see BEST Resource for discussion of each 
142  category of biomarker11): 
143   
144    diagnostic biomarker 
145    monitoring biomarker 
146    pharmacodynamic/response biomarker (e.g., clinical trial endpoints, including surrogate 
147  endpoints) 
148    predictive biomarker 
149    prognostic biomarker 
150    safety biomarker 
151    susceptibility/risk biomarker 
152   
153  The proposed use in drug development should include, as appropriate: 
154    Purpose of use in drug development (e.g., a prognostic biomarker to support enrichment 
155  of Alzheimers Disease clinical study/trial populations, a safety biomarker to evaluate 
156  drug-induced liver injury) 
157    Proposed stage of drug development (e.g., phase 1 clinical trials, nonclinical safety 
158  studies) 
159    Clinical trial population or model system (e.g., healthy adult subjects, patients with 
160  COPD, rats, cultured mouse fibroblasts) 
161    Therapeutic mechanism of action (MOA) for which the biomarker is intended to have 
162  value, provided that the MOA is relevant to the biomarkers biology and intended utility 
163  (e.g., both the MOA and the biomarker are within the same biologic pathway or process) 
164   
165  Accumulating the data to support a biomarker for qualification can take considerable time and 
166  resources.  Often, requestors do not have adequate data and/or information to support their 
167  proposed COU.  One approach is to initially qualify a biomarker for a COU that is limited in 
168  scope to facilitate the integration of the biomarker in drug development, which could result in the 
169  accumulation of additional evidence that can help qualify the biomarker for a COU with a more 
170  expanded scope in the future.  For example, a biomarker could be qualified first as a 
171  pharmacodynamic biomarker for use in dose selection.  After additional information is 
172  accumulated, the same biomarker could ultimately be qualified as a pharmacodynamic biomarker 
173  for use as a clinical trial endpoint; if the biomarker is considered to be reasonably likely to 
                                                 
11 Definition is from the BEST Glossary available at:  https://www.ncbi.nlm.nih.gov/books/NBK326791/ (last 
accessed March 1, 2018). 
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174  predict clinical benefit or has been shown to predict clinical benefit, it could be used as a 
175  surrogate endpoint to support accelerated12 or traditional drug approval, respectively. 
176   
177  C.   Assessment of Benefits and Risks  
178   
179  Biomarker developers are expected to provide a clear and objective description of the anticipated 
180  benefits and risks of the biomarker for the proposed COU, as well as any potential risk 
181  mitigation strategies.13  The overall balance of benefits, risks, and risk mitigation efforts are 
182  critical for determining the strength of evidence sufficient to support qualification. 
183   
184  The potential benefits of a biomarker for use in drug development depend on the biomarkers 
185  proposed COU and the needs assessment.  Biomarker use could benefit individual patients 
186  participating in clinical trials (e.g., earlier identification of toxicity with a safety biomarker) or 
187  general drug development and regulatory decision-making (e.g., a prognostic or predictive 
188  biomarker used to enrich a patient population could reduce the sample size needed to achieve 
189  statistical significance).   
190   
191  The potential risks of qualifying a biomarker should address the consequences of incorrect 
192  decision-making or harm to patients if the correlation between the biomarker and the outcome of 
193  interest does not indicate what it is intended to indicate.  Requestors should consider factors that 
194  might mitigate harm if the biomarker does not perform as expected.  The potential risk is closely 
195  linked to biomarker category and the proposed COU.  For example, if a safety biomarker fails to 
196  accurately predict early toxicity, clinical trial participants might be placed at risk for serious 
197  adverse drug reactions.  Alternatively, the same safety biomarker might jeopardize the successful 
198  development of a promising new drug and prevent significant societal benefits if it erroneously 
199  identifies a risk where none exists.  These risks could be mitigated, in part, by using the proposed 
200  biomarker with existing safety monitoring measures, rather than as a stand-alone assessment for 
201  the toxicity of interest.  In another example, a prognostic biomarker intended for clinical trial 
202  enrichment might fail to identify patients with more rapid disease progression.  In this case, 
203  mitigation strategies could include incorporating an interim analysis for sample size re-
204  estimation.   
205   
206  The following questions should be used to characterize the potential benefits and risks of a 
207  biomarker for a specific COU: 
208   
209  1.  Does the biomarker have the potential to add value to drug development?      
210   
211  2.  What other tools are available for the biomarkers proposed use and what added value 
212  might the biomarker provide?   
213   
                                                 
12 To obtain accelerated approval for a drug, sponsors must meet the statutory criteria in section 506(c) of the FD&C 
Act (21 U.S.C. 356(c)).  Also see 21 CFR part 314, subpart H and part 601, subpart E.  
13 The terms benefit, risk, and risk mitigation that are used in the context of biomarker qualification have specific 
meanings that are relevant to biomarker development and evaluation, and these meanings are separate and distinct 
from how these terms are used in the context of evaluating the safety and effectiveness of medical products. 
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214  3.  What are the anticipated consequences if the biomarker is unsuitable for its proposed 
215  use? 
216   
217  4.  What factors or other tools can mitigate the potential risks of relying on the biomarker for 
218  its proposed use if the biomarker does not perform as expected?   
219   
220  D.  Determining Evidence That Is Scientifically Sufficient To Support COU 
221   
222  The evidence sufficient to qualify a biomarker depends on its COU and the potential benefits and 
223  risks associated with its use.  The benefits and risks associated with a biomarkers COU drives 
224  expectations for the reliability of the biomarker to predict the outcome of interest.  If the 
225  potential benefits far outweigh the potential risks and/or there are acceptable risk mitigation 
226  approaches, there could be increased tolerance for uncertainty.  In such a case, the strength of 
227  evidence expected to support qualification could be lower.  If the potential benefits minimally 
228  outweigh the risks of relying on the biomarker, the strength of evidence expected to support 
229  qualification should be higher.  
230   
231  Ultimately, whether there is sufficient evidence to support qualification of a biomarker for use in 
232  drug development depends on the selection of the appropriate biomarker for the proposed COU, 
233  the quality of the biomarker measurement, and the correlation of the biomarker with the outcome 
234  of interest.  Evidence to support qualification consists of data to support clinical validation and 
235  analytical validation. 
236   
237  Clinical validation establishes that a biomarkers relationship with the outcome of interest is 
238  acceptable for the proposed COU.  The requestor should describe what is known about the 
239  biomarkers role in the causal or outcome pathway of interest, as well as describe knowledge 
240  gaps about the pathophysiology and molecular underpinnings of the disease.  Describing the 
241  biomarkers position in the disease pathway, if applicable, helps to support the biological 
242  plausibility of the biomarkers role in the proposed COU.  The requestor should provide data 
243  supporting the relationship between the biomarker and a clinical outcome that reflects how an 
244  individual feels, functions, or survives.  This relationship should be supported by statistical 
245  analyses (see section V.) and should come from multiple independent data sources.  Together 
246  this information can establish the clinical validity of a biomarker for a specified COU.   
247   
248  Biomarkers considered for qualification are conceptually independent of the specific method of 
249  measurement; however, a biomarker cannot be qualified without a reliable method of 
250  measurement.14  Relevant performance characteristics of the biomarker tests used to support 
251  qualification should be assessed through analytical validation studies to ensure that biomarker 
252  data for qualification is obtained using acceptable measurement methods to support the proposed 
253  COU and that biomarker tests used in drug development for the COU (if different from the tests 
                                                 
14 Qualification of a biomarker does not connote approval or clearance of a diagnostic device or of a companion or 
complementary diagnostic device for use in clinical practice, and it also does not qualify the biomarker for use in 
clinical practice.  The approval/clearance of a biomarker test by the Center for Devices and Radiological Health or 
by CBER also does not indicate qualification of the biomarker for use in drug development.  
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254  used to qualify the biomarker) perform as well as the tests used for biomarker qualification.  
255  Analytical considerations are discussed further in section IV. below.   
256   
257  Clinical validation and analytical validation are distinct processes; however, the two processes 
258  are iterative and dependent on one another.  A reliable test should be used to measure the 
259  biomarker before the biomarker measurement cutoffs15 can be established, and the cutoffs should 
260  be defined before the biomarker test can be analytically validated.  Through this iterative 
261  process, experience with the biomarker and the biomarker test could lead to improvements in the 
262  technical performance of the test and the understanding of the biomarkers biological and clinical 
263  significance.  It is important to have a high level of confidence in the biomarker tests analytical 
264  performance when confirming the relationship between a biomarker and clinical outcome of 
265  interest, and generally, biomarker qualification studies intended to confirm this relationship 
266  should be conducted using a validated test (see Figure 2).  
267   
268  Figure 2:  Biomarker Validation Approach                    
269   
270  The rigor of the analytical and clinical validation performed for biomarker qualification should 
271  support the utility of the proposed COU.  A listing of qualified biomarkers with FDA reviews 
                                                 
15 Cutoff is the value at or above which a biomarker test result is determined to be positive (or in a specific category) 
and the value below which the result is determined to be negative (or in a different category). 
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272  describing the evidence leading to their qualification can be found on the FDA Biomarker 
273  Qualification Program website.16  
274   
275  IV.  ANALYTICAL CONSIDERATIONS  
276   
277  Because drug development decisions will be made based upon qualified biomarkers, any 
278  biomarker test used to measure the biomarker should be robust, sensitive, and specific enough to 
279  support the decisions defined by the COU.   
280   
281  Analytical validation for the purpose of biomarker qualification includes establishing that the 
282  analytical performance characteristics of a biomarker test, such as the accuracy and 
283  reproducibility, are acceptable for the proposed COU in drug development.  This is validation of 
284  the tests technical performance, but is not validation of the biomarkers usefulness. The 
285  biomarker test and associated performance characteristics will vary depending on the biomarker 
286  type (molecular, histologic, radiographic, and physiologic characteristic).  A biomarker test is an 
287  assessment system comprising three essential components:  (1) source or materials for 
288  measurement, (2) an assay for obtaining the measurement, and (3) method and/or criteria for 
289  interpreting those measurements.  All relevant components of the biomarker test should be 
290  assessed in the analytical validation studies and determined to be acceptable (see Figure 2).  
291   
292  Analytical validation of a biomarker test should consider the acceptability of the source or 
293  materials from which the biomarker is measured.  For a molecular or histologic biomarker, for 
294  example, the source includes not only the sample, but also the sample collection, storage, and 
295  processing conditions.  For a radiographic or physiologic biomarker, the source of measurement 
296  could include factors such as the patient preparation and positioning.  Sample collection, 
297  preparation, and storage protocols (as applicable for the biomarker type) should be established 
298  and assessed in the analytical validation studies to determine acceptability. 
299   
300  A reliable biomarker test is also contingent on all components of the biomarker assay, such as 
301  supplies, equipment, software, and instructions.  User instructions/protocols should be 
302  established and followed during validation testing to ensure acceptability.  Additional details 
303  such as reagent versions, lot numbers, and software version should be noted to help identify 
304  modifications to the test that could alter performance.    
305   
306  Biomarker measurements are expressed in many ways (e.g., the concentration of molecular 
307  species in body fluids, cells, or tissues; the presence or extent of features in images obtained 
308  from microscopy or radiology; the magnitude of in vivo physiological signals).  Some of these 
309  measurements are produced directly from a biomarker test, and others are determined by an 
310  interpretation of biomarker test results.  Examples of these interpretations include radiographic 
311  image analysis and the combination of individual biomarker measurements in a defined 
312  algorithm to determine a composite biomarker score.  The measurement interpretation, as with 
313  the other components of the biomarker test, can introduce error into the biomarker measurement; 
                                                 
16 Information on qualified biomarkers is available at 
https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/Biomarker
QualificationProgram/ucm535383.htm. 
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314  therefore, reliable and acceptable interpretation should be established in the analytical validation 
315  studies. 
316   
317  Acceptance criteria for analytical performance characteristics for a biomarker test are set 
318  according to the overarching specifications for the biomarker to support the proposed COU and 
319  according to the risks associated with limitations in the analytical performance of the test.  
320  Inadequate biomarker test performance could lead to incorrect interpretation of a biomarkers 
321  significance, thus undermining the clinical validation of the biomarker.  Bias and dispersion in 
322  the biomarker test lead to uncertainty when interpreting biomarker test results and affect the 
323  value of the biomarker as a drug development tool.  Requestors should consider not only the 
324  proposed COU and potential risks and benefits of the proposed biomarker, but also the following 
325  factors when specifying performance characteristic acceptance criteria for candidate tests:   
326   
327    The performance characteristics of existing measurement methods  
328   
329    The biological variability of the biomarker in the populations of interest, if known   
330   
331    The minimum magnitude of biomarker change expected to affect decisions for the 
332  proposed COU (i.e., cutoff for separating populations or determining change from 
333  baseline)   
334  Considerations for assessing the performance characteristics of biomarker tests for specific types 
335  of biomarkers are beyond the scope of this guidance.  The FDA guidance for industry 
336  Considerations for Use of Histopathology and Its Associated Methodologies to Support 
337  Biomarker Qualification17 provides general considerations regarding performance characteristics 
338  for histologic biomarker methodologies.  The analytical validation studies and performance 
339  characteristics vary greatly according to the technology of the biomarker test.  Many well-
340  accepted protocols are published for examination of analytical performance characteristics for 
341  specific biomarker test methodologies.  Such protocols can be selected and adapted for use in 
342  accordance with a risk-based assessment of the evidentiary stringency determined by the 
343  proposed COU.  
344   
345  V.   STATISTICAL CONSIDERATIONS  
346   
347  The goal of statistical analyses in biomarker qualification is to evaluate the degree and certainty 
348  of association between a biomarker and an outcome of interest.  Consideration should be given to 
349  the design and conduct of studies contributing data to support biomarker qualification, as well as 
350  the statistical analyses conducted.  This section describes the potential sources of data, as well as 
351  study design and statistical considerations when assessing the association between a proposed 
352  biomarker and an outcome of interest for the purposes of biomarker qualification.   
353   
354  Data used to establish the relationship between a biomarker and an outcome of interest, to 
355  support biomarker qualification, can come from a variety of sources including the following:   
                                                 
17 We update guidances periodically.  To make sure you have the most recent version of a guidance, check the FDA 
guidance web page at https://www.fda.gov/RegulatoryInformation/Guidances/default.htm.  
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356   
357    Randomized controlled trial  
358    Single-arm/historical control trial 
359    Cohort study 
360    Case-control study (including nested) 
361    Cross-sectional study  
362    Case series or case reports 
363    Registry information 
364    Meta-analysis 
365   
366  The strongest level of evidence to support the association of a biomarker with an outcome of 
367  interest comes from prospective studies that are specifically designed and powered to assess the 
368  association.  In many settings, however, data from studies conducted for other purposes are used 
369  to support biomarker qualification.  Ultimately, the COU, with its associated potential benefits 
370  and risks, determines what types of data may be acceptable to support qualification; clinical trial 
371  data is not critical for all COUs.  Regardless of the data sources proposed to support the 
372  biomarkers COU, biomarker developers should consider the potential methodological 
373  limitations that could lead to overestimation of any actual associations, including lack of proper 
374  control for bias, confounding, and multiplicity, and address these limitations in their analysis 
375  plan.  Verification of the results with an independent data source increases the credibility of the 
376  results.   
377   
378  Although the recommendations provided in the ICH guidance for industry E9 Statistical 
379  Principles for Clinical Trials18 are primarily intended for late-stage interventional clinical trials, 
380  many of the principles described in ICH E9 are also relevant when considering the data intended 
381  to support biomarker qualification.  Specifically, the principles are as follows:  
382   
383    To the extent possible, the sample size should be sufficient to ensure adequate power to 
384  assess a clinically relevant association between the biomarker and the outcome of interest 
385  with reasonable dispersion.  Sample-size considerations could be based on a single study 
386  or multiple studies considered in aggregate, and it is recognized that flexibility might be 
387  needed in certain clinical contexts (e.g., rare diseases).  
388   
389    The analysis plan should control for multiplicity and consider the potential for false 
390  positive results.  Multiplicity commonly occurs when analyzing multiple-candidate 
391  biomarkers and could lead to overestimation of biomarker associations with the clinical 
392  outcome of interest.   
393   
394    The strength of the relationship between the biomarker and the outcome of interest 
395  should be quantified appropriately.  Over-reliance on p-values should be avoided. 
396   
                                                 
18 Available on the FDA Drugs guidance web page at 
https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm.  
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397    Potential sources of bias should be identified and strategies to minimize bias should be 
398  described.  For example, when possible, the biomarker analysis plan should be developed 
399  before unblinding of the data and access to subjects biomarker status for purposes of the 
400  analysis.  In some situations, clinical outcome data might have already been unblinded 
401  and analyzed, but the initial analyses did not include the biomarker data (i.e., if samples 
402  were collected for later use) or the analyses recommended to support qualification were 
403  not performed.  Although such data could be used to support qualification, the analyses 
404  intended to support biomarker qualification should be specified in an analysis plan with a 
405  prospective-retrospective design before analyzing the data.    
406   
407    Consideration should be given to sample and data collection methods, including 
408  strategies to minimize and account for the effect of missing data, and these methods 
409  should be included in the analysis plan.  When collection of biomarker data is only from 
410  a subset of clinical sites, groups, or treatments, this non-randomized sampling 
411  (convenience sampling) might be statistically problematic if the subset is somehow 
412  partial to the outcomes being studied, yielding biased estimates with unknown 
413  characterization of the bias.     
414   
415    Innovative statistical approaches such as adaptive designs and Bayesian designs, 
416  including prior information and hierarchical models, can be considered for qualification 
417  of biomarkers.  
418  Data supporting biomarker qualification are often based in part on the published literature and, in 
419  some situations, could be exclusively based on the published literature.  It is critical for the 
420  biomarker developer to identify the limitations and gaps in these data and address how they 
421  affect the interpretability of the results.  In addition, when using published literature, the criteria 
422  for study inclusion should be specified a priori in a systematic study protocol of the published 
423  literature, to avoid publication or selection bias.  
424    
425  When assessing whether the association between a biomarker and an outcome of interest is 
426  acceptable for the proposed COU, a key consideration is how to define the outcome of interest.  
427  In some settings, there might not be a current standard outcome, or a standard outcome with 
428  known limitations is used for comparative purposes.  For example, changes in serum creatinine 
429  are widely used in biomarker development as the current standard for predicting drug-induced 
430  kidney injury.  However, changes in serum creatinine levels are neither highly sensitive nor 
431  highly specific for drug-induced kidney injury.  In a setting in which the current standard 
432  outcome has significant limitations or a current standard outcome does not exist, it is important 
433  to consider the totality of all available data that may provide sufficient support to establish that 
434  the biomarker can be acceptably relied upon for the proposed COU.  Each biomarker 
435  qualification submission has unique challenges that call for careful clinical and statistical 
436  considerations that may lead to distinct solutions. 
437   
438  There are multiple statistical approaches to assessing the association between a biomarker and 
439  clinical outcome measures.  For binary outcome measures, such as the presence or absence of 
440  disease, results can be evaluated using clinical sensitivity and specificity, and positive and 
441  negative predictive values, or by evaluating receiver operating characteristic curves.  For 
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442  continuous outcome measures, such as disease progression, results can be evaluated using 
443  regression models.  When appropriate, adjusted or composite biomarkers can be considered with 
444  adequate justification, including biomarkers derived from composite measurements, covariate-
445  adjusted measurements, change from baseline measurements, and repeated measurements. 
446   
447  When continuous data will be dichotomized, the relationship between the clinical outcome and 
448  the biomarker could be initially established quantitatively.  Expressing biomarker measures 
449  quantitatively increases the statistical power compared to dichotomization when establishing 
450  such a relationship.  Once a relationship between a biomarker and an outcome of interest has 
451  been established, several cutoffs on a continuous biomarker can be considered.  The most 
452  appropriate cutoffs can then be selected by comparing the clinical outcomes of at risk subjects 
453  with each different biomarker cutoff.  The choice of a cutoff can also be informed by the benefit-
454  risk tradeoff of the decisions made based on the biomarker and the proposed COU (e.g., selecting 
455  a cutoff that gives more weight to clinical sensitivity versus a cutoff that gives more weight to 
456  clinical specificity).  In some instances, selecting a specific cutoff might not be appropriate, and 
457  describing a spectrum of threshold values for the biomarker could be more informative.  For 
458  example, in the case of an enrichment biomarker, submissions might describe a spectrum of 
459  cutoffs in a model representing the potential increase in power to be gained from enrichment, 
460  which should be considered against potential enrollment challenges resulting from a narrowed 
461  patient population. 
462   
463  There are no set quantitative criteria for determining whether the relationship between the 
464  biomarker and the clinical outcome is sufficiently strong to support biomarker qualification.  
465  Criteria based on parameters used to quantify the relationship, such as the threshold values for 
466  sensitivity and specificity, and the presence of a gradient (e.g., clinical performance change as 
467  function of biomarker quantity) can provide confidence that a finding is likely to be relevant, 
468  reliable, and statistically robust.  Additional considerations that support the biomarkers 
469  association with the clinical outcome should also be assessed, such as whether there is a strong 
470  biological rationale supporting the role of the biomarker in the proposed COU and whether the 
471  findings are supported by more than one investigation or analysis set or there are multiple lines 
472  of evidence (e.g., experimental models and human studies).  Together, the strength of the data 
473  supporting the association and additional considerations should be evaluated to determine 
474  whether the evidence supporting the relationship between the biomarker and the clinical outcome 
475  is adequate to support biomarker qualification.  This determination will be dependent on the 
476  evidentiary framework assessment for each individual submission described in section III.  
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