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.
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.
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.
We have compiled a number of frequently asked questions to answer your inquiries about Controlled Terminology.
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 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 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.
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.
A substance administration consists of a substance and the activity of administering the substance. Some data items describe the substance, others the administration.
This is an example for the familiar test Temperature.
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.
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.
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)
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.
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.