A single person may participate in a study multiple times. A common scenario for multiple participations is when a screening failure subject later goes through the screening process again in another attempt to enroll. There are also studies where a subject may participate more than once with more than 1 body part, such as a subject who participates with the left eye, and then enrolls again with the right eye. Some studies of rare diseases allow a subject to participate in and complete a study, and then later participate again. Subjects may receive different treatments in these different participations.
If the safety analysis will report adverse events, clinical events, or laboratory grading according to a standard toxicity grading scale (e.g., Common Terminology Criteria for Adverse Events, CTCAE), then analysis toxicity variables should be implemented. These toxicity variables may be copied directly from SDTM as predecessor variables or they may be imputed or assigned based on the statistical analysis plan and sponsor requirements.
If an analysis is assessing low toxicity separately from high toxicity, then bidirectional toxicity variables should be used in the ADaM dataset. Toxicity variable ATOXGR may be used in addition to the bidirectional toxicity variables to capture shifts from low to high or high to low within a parameter. The low and high toxicity grade variables may be copied directly from SDTM as predecessor variables or they may be imputed or assigned based on the statistical analysis plan and sponsor requirements (e.g., unidirectional toxicity variables).
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)
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 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.