Findings results as originally collected may not be review ready for a variety of reasons, so SDTM has a variable, --ORRES, for the result as originally collected, and another variable, --STRESC, for the result in ready-for-review form.
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.
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.
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,
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.
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.
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.
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.
"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).
This article provides a good example for documenting study subject site transfers, using existing SDTMIG domains and minimal supplemental qualifiers. Originally modeled for COVID studies, this approach could be used any time a study subject change sites during their participation in the study.
When the Guidance for Ongoing Studies Disrupted by COVID-19 was being developed, one of the issues was how to represent subject visits, given that regulators wanted to know about visits that were missed or modified due to the pandemic.
The CDASHIG section "PE - Physical Examination" describes a best practice for collecting physical examination data. Basically, any abnormalities would be recorded as medical history or adverse events, depending on timing whether an exam was performed would be recorded by treating the exam as a procedure.
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.
The current Immunogenicity Specimen Assessments (IS) domain in the SDTMIG v3.4 is designed to represent data pertaining to specimen-based assessments that measure the “presence, magnitude and scale of the immune response upon an antigen stimulation or encounter.” Not only does the new domain definition better align with the scientific definition of “immunogenicity”, but it also expands the scope of the IS domain from the previous versions of SDTMIG (i.e., v3.2 and v3.3), where the IS domain was defined to represent data pertaining to “assessments that describe whether a therapy provoked/caused/induced an immune response”.