FDA-NIH TERMINOLOGY FOR
CLINICAL RESEARCH
Glossary of Terms and Deﬁni�ons

FDA-NIH Clinical Research Working GroupGlossary Terms and Deﬁni�ons
Introduc�on:
The U.S. Food and Drug Administra�on and the Na�onal Ins�tutes of Health (NIH) Joint LeadershipCouncil convened a working group to bring clarity to terms that are inconsistently used within thescien�ﬁc community. Working together with the goals of improving communica�on and scien�ﬁcunderstanding, the two agencies developed a glossary with deﬁni�ons of terms used in clinical research.
To fulﬁll their charge, the FDA-NIH Clinical Research Working Group (CRWG) developed deﬁni�ons for 37
clinical research terms related to innova�ve clinical study designs, including studies using real-world data
(RWD) to generate real-world evidence (RWE), that support scien�ﬁc, clinical, and/or regulatory
decision-making. In addi�on, 8 terms are included for reference and FDA-NIH are not seeking commenton these 8 terms. The membership of the FDA-NIH CRWG consisted of sta�s�cians, epidemiologists,
pharmacologists, clinicians, biomedical engineers, and policy experts from both agencies.
This document is intended to facilitate communica�on within the clinical research community, by
helping establish a common vocabulary to more uniformly characterize clinical research, including
innova�ve trial designs and studies using RWD to generate RWE. In turn, the community may be beter
situated to evaluate poten�al strengths and weaknesses of individual studies, and to convey innova�ve
and other aspects of clinical research in a meaningful way to funders, reviewers, and other interestedpar�es.
Terms in the glossary are grouped into the following two areas:
1. Terms for Comment: These terms include approaches to clinical study design, methodology, andinterpreta�on and descrip�on of research results. Where an exis�ng deﬁni�on is used or
adapted, the source(s) are listed.
2. Terms for Reference: These are terms with well-established deﬁni�ons (e.g., well-established in
the scien�ﬁc community) included for completeness and are not newly deﬁned with this project.
They are included as Appendix A to add context for the terms the FDA-NIH CRWG have deﬁned
under the “Terms for Comment”.

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Glossary Terms and Deﬁni�ons
Terms for CommentA
Administra�ve Claims Data
The informa�on obtained from claims that health care providers submit to insurers to receive payment
for treatments and other interven�ons. Claims data use standardized medical coding systems(nomenclatures), such as the World Health Organiza�on Interna�onal Classiﬁca�on of Diseases Coding(ICD-CM) to iden�fy diagnoses, Na�onal Drug Code (NDC) to iden�fy drugs, and Current Procedural
Terminology (CPT®) to iden�fy procedures.

C
Causal EffectA measure of diﬀerence in outcome that would be expected in individuals subjected to an exposure ofinterest compared to the expected outcome if those same individuals were subjected to a speciﬁed
alterna�ve exposure (including no exposure).
Source: Adapted from Musci R.J. & Stuart, E. (2019). Ensuring causal, not casual, inference. Prevention
Science, 20, 452–456, htps://doi.org/10.1007/s11121-018-0971-9.
Causal Inference
The process of evalua�on, es�ma�on, and atribu�on of a causal effect.
Cluster Randomized Trial
A trial in which randomiza�on is at a group level (for instance, by community, health care facility or
medical provider) rather than an individual level.
Source: Adapted from the Secretary’s Advisory Commitee on Human Research Protec�ons,
Recommenda�ons on Regulatory Issues in Cluster Studies. (2014). Available at: Attachment C:
Recommendations on Regulatory Issues in Cluster | HHS.gov.
Common Data Element
A standardized, precisely defined variable or question that is paired with a set of specificallowable values or responses, that are used systematically across different sites, studies,or clinical trials to ensure consistent data collection and/or analysis.
Source: Adapted from the NIH CDE Repository.

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Glossary Terms and Deﬁni�ons
Common Data Model (CDM)
Comprehensive framework that includes deﬁni�ons, speciﬁca�ons, and opera�onal rules for data to bepresented and used in a common manner to enable interoperability.
Source: Adapted from Duke Margolis Center for Health Policy, Characterizing RWD Quality and Relevancy
for Regulatory Purposes. Available at htps://healthpolicy.duke.edu/sites/default/ﬁles/202003/characterizing_rwd.pdf.
Completeness of Capture
The extent to which a data source includes a complete representa�on of the exposures, outcomes, andcovariates needed for the proposed analysis. Incomplete capture may be due to variables that were notrecorded or variables that include some missing values.
Computable Phenotype
A clinical condi�on or characteris�c that can be ascertained using a computerized query to Electronic
Health Record (EHR) system, administra�ve claims database, or clinical data repository using a deﬁned
set of data elements and logical expressions. Computable phenotype deﬁni�ons provide thespeciﬁca�ons for iden�fying popula�ons likely to have the condi�ons or characteris�cs of interest.
Source: Adapted from FDA dra� guidance Real-World Data: Assessing Electronic Health Records andMedical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products(September 2021). When ﬁnal, this guidance will represent FDA’s current thinking on these topics.
Confounding
Systema�c error in es�ma�on of the measure of the eﬀect of a medical product on an outcome due toanother factor that is associated with both the exposure and the outcome and not through the causal
pathway between exposure and outcome.
Source: Adapted from Porta M. et al, A Dic�onary of Epidemiology. (2014). United Kingdom: Oxford
University Press.
Con�nuity of Coverage
The period of �me over which an individual is enrolled in a health care system (provider, pharmacy,
insurer, or other) and for which data on provided or reimbursed healthcare services and treatments arecaptured in that system.
Source: Adapted from FDA dra� guidance Real-World Data: Assessing Electronic Health Records andMedical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products(September 2021). When ﬁnal, this guidance will represent FDA’s current thinking on these topics.

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Glossary Terms and Deﬁni�ons
D
Data Cura�on
Processing of source data (unstructured and/or structured data) into a dataset suitable for analyses. The
cura�on process involves the applica�on of standards for the exchange, integra�on, sharing, and retrievalof source data, o�en from various sources. For example, the applica�on of standard medical diagnos�c
codes to adverse events, disease staging, the progression of disease, and other medical and clinicalconcepts.
Source: Adapted from FDA ﬁnal guidance CVM GFI #266 Use of Real-World Data and Real-World Evidence
to Support Eﬀectiveness of New Animal Drugs (October 2021).
Data Harmoniza�on
The process of combining data from diﬀerent sources and reorganizing it according to a single schema sothat data are compa�ble and comparable. Data are combined by either iden�fying equivalent data
elements between the sources or by applying speciﬁc transforma�ons between the elements to derive acommon data element.
Source: Adapted from the Na�onal Ins�tute of Environmental Health Sciences, Common Language
Glossary from the Environmental Health Language Collabora�ve.
Data Imputa�on
A process using sta�s�cal techniques to es�mate missing data values, facilita�ng subsequent analyses.
Source: Adapted from Food and Agricultural Organiza�on of the United Na�ons, Sta�s�cal Standard Series
Imputa�on Version 2.0. Available at: htps://www.fao.org/3/cb9339en/cb9339en.pdf.
Data Lake
A controlled, centralized environment that stores structured and unstructured data in its na�ve form andprovides infrastructure for organizing large volumes of diverse data from mul�ple sources.
Source: Adapted from Amazon AWS.
Data Transforma�on
The process of conver�ng data from one format or structure into another format or structure. It is aprocess of data extrac�on and conversion or normaliza�on in construc�on of analy�c datasets.
Source: Adapted from FDA ﬁnal guidance CVM GFI #266 Use of Real-World Data and Real-World Evidence
to Support Eﬀectiveness of New Animal Drugs (October 2021).

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Glossary Terms and Deﬁni�ons
Data Warehouse
A controlled, centralized environment that stores structured data in a processed form for analysis andprovides infrastructure for data access by mul�ple applica�ons.
Source: Adapted from FDA dra� guidance Real-World Data: Assessing Electronic Health Records andMedical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products(September 2021). When ﬁnal, this guidance will represent FDA’s current thinking on these topics.
Distributed Data Network
A network in which data from mul�ple sites are transformed into a single common data model with theability to execute a query without substan�al modiﬁca�ons on mul�ple datasets.
Source: Adapted from FDA dra� guidance Real-World Data: Assessing Electronic Health Records andMedical Claims Data To Support Regulatory Decision Making for Drug and Biological Products(September 2021). When ﬁnal, this guidance will represent FDA’s current thinking on these topics.

E
Electronic Health Record
An individual pa�ent record contained within an electronic system. A typical individual record may includea pa�ent medical history, diagnoses, treatment plans, immuniza�ons, allergies, imaging, pharmacy
orders, laboratory values, and test results.
Source: Adapted from FDA dra� guidance Real-World Data: Assessing Electronic Health Records andMedical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products(September 2021). When ﬁnal, this guidance will represent FDA’s current thinking on these topics.

I
Immortal TimeA span of �me in the observa�on or follow-up period of a cohort during which the outcome under study
could not have occurred, due to the cohort design and/or exposure deﬁni�on.
Source: Adapted from Suissa, S. (2008). Immortal �me bias in pharmacoepidemiology, American Journalof Epidemiology, 167(4),492–499, htps://doi.org/10.1093/aje/kwm324.

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Glossary Terms and Deﬁni�ons
Informa�on Bias
Systema�c error in es�ma�on of an associa�on or other parameter of interest arising from measurement
error in the data. For categorical variables, measurement error is usually called classiﬁca�on error or
misclassiﬁca�on.
Source: Adapted from Daniel, G., Silcox, C., Bryan, J., McClellan, M., Romine, M., & Frank, K. (2018).
Characterizing RWD Quality and Relevancy for Regulatory Purposes. Duke Margolis Center for Health
Policy, htps://healthpolicy.duke.edu/sites/default/ﬁles/2020- 03/characterizing_rwd.pdf and Rothman
KJ, Greenland S, Lash TL, Lippincot Williams & Wilkins. (2008). Modern Epidemiology.
Interven�onal Study
A study involving par�cipants (e.g., healthy individuals or individuals with a disease or condi�on ofinterest) whose exposure or interac�on with a medical product is assigned according to a study protocol
to evaluate the eﬀect on health outcomes or product performance.
Source: Adapted from FDA dra� guidance Considera�ons for the Use of Real-World Data and Real-World
Evidence to Support Regulatory Decision-Making for Drug and Biological Products (December 2021).
When ﬁnal, this guidance will represent FDA’s current thinking on these topics.

M
Missing Data
Data that would have been used in the study analysis but were not observed, collected, or accessible.
This refers to informa�on that is intended to be collected but is absent and informa�on that is notintended to be collected and is therefore absent. There may be special considera�ons regarding realworld data sources (e.g., electronic health records or claims); such data are generally not collected forprimary research purposes and therefore may not have systema�c data capture to answer a researchques�on.
Source: Adapted from FDA dra� guidance Real-World Data: Assessing Electronic Health Records andMedical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products(September 2021). When ﬁnal, this guidance will represent FDA’s current thinking on these topics.

N
N of 1 Trial
A clinical trial evalua�ng an interven�on or mul�ple interven�ons in a single par�cipant according to a
protocol in which there is either switching between interven�on(s) and control or a planned comparison
between an interven�on and a natural history.

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Glossary Terms and Deﬁni�ons
Non-interventional (observational) study
A type of study in which individuals are not assigned to a medical product according to a protocol.
Source: Adapted from FDA ﬁnal guidance Use of Real-World Evidence to Support Regulatory DecisionMaking for Medical Devices (August 2017) and FDA dra� guidance Considera�ons for the Use of RealWorld Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological
Products (December 2021). When ﬁnal, this guidance will represent FDA’s current thinking on these
topics.

O
Observa�onal Study, Retrospec�ve
A study that iden�ﬁes the popula�on and determines the exposure/treatment from data collected before
the ini�a�on of the study. The variables and outcomes of interest are determined at the �me the study isdesigned.
Source: Adapted from the Framework for FDA’s Real-World Evidence Program (December 2018) and FDA
ﬁnal guidance Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices(August 2017).

P
Pragma�c Clinical Trial
A clinical trial designed to eﬃciently inform decision-making on the beneﬁts, burdens, and risks of healthinterven�ons in representa�ve popula�ons by including pragma�c elements (see deﬁni�on below) that
1) are par�ally or fully integrated into rou�ne clinical prac�ce and/or 2) that streamline trial design andconduct.
Source: Adapted from Caliﬀ R.M., Sugarman J. (2015). Exploring the ethical and regulatory issues inpragma�c clinical trials. Clinical Trials, (5), 436-441,
htps://journals.sagepub.com/doi/10.1177/1740774515598334.
Pragma�c Elements
Design features that can be integrated into a clinical trial, including but not limited to, one or more of thefollowing elements: broad eligibility criteria, simpliﬁed recruitment and follow-up, ﬂexibility in delivery of
the interven�on (e.g., community se�ngs), ﬂexibility in assessment frequency, and measurement ofoutcomes relevant to the popula�on.

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Glossary Terms and Deﬁni�ons
Propensity Score
The condi�onal probability of assignment to a par�cular treatment given a set (e.g., vector) of observed
covariates.
Source: Adapted from Rosenbaum, P.R., Rubin, D.B. (1983). The central role of the propensity score in
observa�onal studies for causal eﬀects, Biometrika, 70(1), 41–55,
htps://doi.org/10.1093/biomet/70.1.41.

R
Registry
An organized system that collects clinical and other data in a standardized format for a popula�on deﬁned
by a par�cular disease, condi�on, or exposure.
Source: Adapted from FDA ﬁnal guidance Real-World Data: Assessing Registries To Support Regulatory
Decision-Making for Drug and Biological Products (December 2023) and FDA ﬁnal guidance Use of RealWorld Evidence to Support Regulatory Decision-Making for Medical Devices (August 2017).
Residual Confounding
Confounding (see definition) that remains a�er adjus�ng for measured confounders.
Source: Adapted from Porta, M. A dic�onary of epidemiology (6th ed.),https://www.oxfordreference.com/display/10.1093/acref/9780199976720.001.0001/acref9780199976720-e-1649.

S
Selec�on Bias
Systema�c error in es�ma�on of an associa�on or other parameter that occurs from factors that inﬂuence
study par�cipa�on [or eligibility].
Source: Adapted from Rothman KJ, Greenland S, Lash TL, Lippincot Williams & Wilkins. (2008). Modern
Epidemiology.
Sequen�al, Mul�ple Assignment, Randomized Trial (SMART)A trial designed to evaluate a collec�on of interven�ons guided by a sequence of decision rules thatspeciﬁes when and how the type and/or intensity of an interven�on should be modiﬁed depending on
the pa�ent’s past or present characteris�cs and/or ongoing clinical state or performance (e.g., response,
adherence) to op�mize clinically important outcomes. In such a trial, pa�ents move along mul�ple
stages and are randomly assigned to one of several suitable interven�on op�ons at each stage.
Source: Adapted from FDA ﬁnal guidance Interac�ng with the FDA on Complex Innova�ve Trial Designs for
Drugs and Biological Products (December 2020).

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Glossary Terms and Deﬁni�ons

Stepped Wedge Cluster Randomized Trial
In a stepped wedge group-randomized trial, also called a stepped wedge cluster randomized trial, groups
or clusters are randomized to sequences that direct them to switch from a control to the interven�on atpredetermined �me points in a sequen�al, staggered fashion un�l all clusters receive the interven�on.
Source: Adapted from Home | Research Methods Resources (nih.gov).
Synthe�c Data
Data that have been created ar�ﬁcially (e.g., through sta�s�cal modeling, computer simula�on) so thatnew values and/or data elements are generated. Generally, synthe�c data are intended to represent thestructure, proper�es and rela�onships seen in actual pa�ent data, except that they do not contain any
real or speciﬁc informa�on about individuals.

T
Target Trial Emula�on
A framework for designing and analyzing an observa�onal study based on conceptualizing a target
randomized trial to answer a scien�ﬁc ques�on and designing the observa�onal study to mimic the triales�mand(s) (including speciﬁca�on of popula�on eligibility criteria, treatment strategies and assignment
procedures, outcomes, handling of intercurrent events, and follow-up period).
Time-Related Bias
Systema�c error in es�ma�on of an associa�on or other parameter of interest due to misclassiﬁca�on or
exclusion of person-�me atributed to the treatment, interven�on, or exposure. Examples include
protopathic bias, latency �me bias, immortal �me bias, �me-window bias, deple�on-of-suscep�bles,
immeasurable �me bias, and other such biases.

U
Umbrella Trial
Trial designed to evaluate mul�ple medical products in separate sub-studies concurrently for a single
disease or condi�on.
Source: Adapted from FDA dra� guidance Master Protocols for Drug and Biological Product Development| FDA (December 2023). When ﬁnal, this guidance will represent FDA’s current thinking on these topics.

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Glossary Terms and Deﬁni�ons

Appendix A
Terms for Reference – These terms are included because they provide context for terms under “Terms forComment”. We are not reques�ng comment on these terms.
Adap�ve Design
A clinical trial design that allows for prospectively planned modifications to one or more
aspects of the design based on accumulating data from subjects in the trial.
Source: FDA ﬁnal guidance Adap�ve Design Clinical Trials for Drugs and Biologics Guidance for Industry(December 2019).
Basket Trial
Trial designed to evaluate a medical product for diﬀerent diseases, condi�ons, or disease subtypes.
Source: FDA dra� guidance Master Protocols for Drug and Biological Product Development | FDA(December 2023). When ﬁnal, this guidance will represent FDA’s current thinking on these topics.
Conceptual Deﬁni�on
Explains a study construct (e.g., exposure, outcomes, covariates) or feature in general or qualita�ve
terms.
Source: FDA dra� guidance Real-World Data: Assessing Electronic Health Records and Medical Claims Data
To Support Regulatory Decision-Making for Drug and Biological Products (September 2021). When ﬁnal,
this guidance will represent FDA’s current thinking on these topics.
Master Protocol
A protocol designed with mul�ple sub-studies, which may have diﬀerent objec�ves and involve
coordinated eﬀorts to evaluate one or more medical products in one or more diseases or condi�ons
within the overall study structure.
Source: FDA dra� guidance Master Protocols for Drug and Biological Product Development | FDA(December 2023). When ﬁnal, this guidance will represent FDA’s current thinking on these topics.

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Glossary Terms and Deﬁni�ons
Observa�onal Study, Prospec�ve
A study in which the popula�on of interest is iden�ﬁed at the start of the study, and exposure/treatment
and outcome data are collected from that point forward. The start of the study is deﬁned as the �me theresearch protocol for the speciﬁc study ques�on was ini�ated.
Source: Framework for FDA’s Real-World Evidence Program (December 2018).
Opera�onal Deﬁni�on
The data-speciﬁc opera�on or procedure a researcher followed to measure constructs in a par�cularstudy.
Source: FDA dra� guidance Real-World Data: Assessing Electronic Health Records and Medical Claims Data
To Support Regulatory Decision-Making for Drug and Biological Products (September 2021). When ﬁnal,
this guidance will represent FDA’s current thinking on these topics.
Pa�ent Generated Health Data
Health-related data created, recorded, or gathered by or from pa�ents, family members, or othercaregivers to help address a health concern.
Source: Deﬁni�on adapted from htps://www.healthit.gov/topic/scien�ﬁc-ini�a�ves/pcor/pa�entgenerated-health-data-pghd and included in FDA dra� guidance Use of Real-World Evidence to Support
Regulatory Decision-Making for Medical Devices | FDA (December 2023). When ﬁnal, this guidance will
represent FDA’s current thinking on these topics.
Pla�orm Trial
Trial designed to evaluate mul�ple medical products for a disease or condi�on in an ongoing manner, withmedical products entering or leaving the pla�orm.
Source: FDA dra� guidance Master Protocols for Drug and Biological Product Development | FDA(December 2023). When ﬁnal, this guidance will represent FDA’s current thinking on these topics.

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