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Controlled Terminology: FAQs

We have compiled a number of frequently asked questions to answer your inquiries about Controlled Terminology.

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Why is an Image like a Specimen?

If you're trying to figure out how to represent imaging data in SDTM, it may be helpful to think about the similarities between an image and a specimen.

A sample taken from a subject for testing at a lab is a surrogate for the subject. Results of tests on the specimen tell us something about the subject at the time the specimen was taken.

CDISC-Annotated CRF Repository in Japanese

A Summary of the Project

The Japan Agency for Medical Research and Development (AMED) was established in 2015 for the advancement of medical discoveries that make life better for everyone. Working under the Prime Minister’s Cabinet and national ministries, AMED provides a single avenue for researchers and institutions seeking funding for medical research and development.

Special Values: "MULTIPLE" and "OTHER"

The SDTMIG directs that, under certain circumstances, variables can be populated with the values "MULTIPLE" or "OTHER". Neither of these values is what might be called a "proper" value for the variable (i.e., a value that provides the the kind of information intended to be represented in the variable). Instead, these special values indicate that there are either multiple proper values or that the proper value collected was not in the list of values presented on the data collection form.

Why Not Just Use SNOMED?

SNOMED (short for SNOMED Clinical Terms or SNOMED CT) is a set of medical terms used widely in clinical practice. Some have asked why CDISC develops its own Controlled Terminology, rather than using SNOMED. There are a number of reasons why we develop terminology:

Domain vs. Dataset: What's the Difference?

The terms “domain” and “dataset” are commonly used in CDISC’s nomenclature and found frequently in the Study Data Tabulation Model (SDTM). For example, the SDTM v1.8 includes 134 instances of domain” and says "A collection of observations on a particular topic is considered a domain." The model includes 78 instances of dataset and certain structures in the model are called "datasets" rather than "domains." Is there a difference between a domain and a dataset?

A Short History of CDISC and SAS Transport Files

When development of the SDTM and SDTMIG started, SAS was in almost universal use in the pharmaceutical industry and at FDA.

Sex and Gender

"Sex" and "gender" are similar but different concepts whose definitions and meanings can be confusing (see, for example, the article Sex and gender: What is the difference? from Medical News Today).

Study Subject vs. Experimental Unit

The BRIDG model makes a distinction between a study subject and an experimental unit. In most studies for which SDTM is implemented, these terms refer to the same person or animal, but there are studies where the study subject is different from the experimental unit. For those studies, it can be useful to understand these subtly different terms.

Domains are Topic-based, Except When They're Based on Structure

For an implementer trying to decide where data belong in SDTM-based datasets, it's pretty clear when data belongs in a trial design dataset, a relationship dataset, one of the new study reference datasets, or one of the special purpose domains. However, it can be difficult to choose the right general observation class dataset, especially if data are about findings.
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Standardized Lab Units

The International System of Units (SI), commonly known as the metric system, is the international standard for measurement. According to the National Institute of Science and Technology (NIST), the SI rests on a foundation of seven defining constants: the cesium hyperfine splitting frequency, the speed of light in vacuum, the Planck constant, the elementary charge (i.e., the charge on a proton), the Boltzmann constant, the Avogadro constant, and the luminous efficacy of a specified monochromatic source.
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Changing Event Severity

In the diagrams below, the red line represents a graph of severity over time for a hypothetical event. For most adverse events, severity cannot be measured on a continuous scale; this line represents hypothetical actual severity, not data that could be recorded. The horizontal lines divide severity into the three categories, "Mild", "Moderate", and "Severe", which are used to describe adverse event severity.
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UCUM and CDISC Codelists

Unified Code for Units of Measure (UCUM) was developed by Regenstrief Institute and the UCUM Organization as an unambiguous system of units and their combinations. UCUM is intended to include all units of measure currently used internationally in science, engineering and business and has been adopted internationally by IEEE, DICOM, LOINC, and HL7, and is also in the ISO 11240:2012 standard.

LOINC and the SDTM

LOINC is a pre-coordinated laboratory coding system used in healthcare IT systems. It includes lab tests, clinical measures, HIPAA documents and standardized survey instruments. It also contains terms for human clinical research but its scope goes beyond research use. LOINC is used in over 170 countries and is mandated in 30.

SDTM Structure Diagrams

SDTM describes several types of datasets. This diagram illustrates hierarchical view of these types of datasets. Findings may be findings about a study subject or about an associated person. A finding record can be linked to supplemental qualifiers, to comments, or to other records via relationships represented in RELREC.

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Novice

Concept Maps for Adverse Events with Increasing Levels of Detail

A query about adverse events is, at heart, an observation. Data on the adverse event may also include location and pattern. This concept map includes those details, as well as terminology that would be used in SDTM.
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Concept Maps for Substance Administration with Increasing Levels of Detail

A substance administration consists of a substance and the activity of administering the substance. Some data items describe the substance, others the administration.
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Concept Maps for a Finding with Increasing Levels of Detail

This is an example for the familiar test Temperature.
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Translating CDASH "PRIOR" and "ONGO" to SDTM relative timing variables

If data is collected in a log form, and if you know the range of dates or visits for collection of log data then The date or visit at which the log is initiated can be used to populate STTPT and CDASH PRIOR or ONGO can be used to populate STRTPT, The date or visit at which the log is finalized can be used to populate ENTPT and CDASH PRIOR or ONGO can be used to populate ENRTPT. STRF and ENRF are not needed and should not be used.
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When Did That Happen? A Brief Guide to Representing Timing in SDTM.

In ordinary conversation, depending on what “that” is, the question, “When did that happen?” could be answered in many ways. The fact that there are so many ways to say when something happened helps to explain why there are so many timing variables in SDTM.
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Novice

Pre-specified Events and Pre-specified Findings

Pre-specified Events Collection of adverse event, clinical events, and medical history events can follow two approaches: Were there any events? If yes, what were the events? Did event X occur? If yes, record the details of the event(s)
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Avoiding SDTM and ADaM Dataset and Variable Name Conflicts

ADaM datasets include names that start with "AD", therefore "AD" must not be used as the name of a custom SDTM domain. Analysis datasets that are not based on ADaM may have names that start with "AX", so "AX" must not be used as the name of a custom SDTM domain. The SDTM Domain Abbreviations codelist includes "AD" and "AX" as a reminder that these domain abbreviations must not be used for SDTM custom domains.
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Assessing Causality

This diagram illustrates the steps that go into assessing the causality of an adverse event. For certain kinds of adverse events, some steps are almost automatic (e.g., an infectious disease can't happen without a pathogen), but for other kinds of adverse events, there may be many possible causes, and the steps can be quite distinct.
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Novice