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?
Standard(s):
ADaM,
BRIDG,
CDASH,
Controlled Terminology,
Glossary,
LAB,
Medical Devices,
PGx,
SDTM,
SDTMIG,
SEND
Novice
Data about medical history and prior meds are often collected at an initial study visit. Records in an SDTM-based dataset for these events and interventions will include information about their starts and ends, either in dates or relative timing variables, and will usually also include --DTC,
Historically, CDISC standards have primarily been used for regulatory submissions of clinical trials data in support of approval to market medical products. However, recent expansion of CDISC standards through therapeutic area user guide (TAUG) development and an increase in CDISC visibility has led to the recognition of the value of data standards in other areas of medical research as well.
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.
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)
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.
The SDTMIG’s description of time point variables covers two different use cases:
1. A planned set of findings scheduled relative to a reference time point, usually a dose of study treatment.
2. A planned number of repeated measurements.