This article was written to explain why there are so many Analysis Data Model (ADaM) documents and to help the ADaM user see how they have been designed to work together.
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”.
Therapeutic Area User Guides (TAUGs) contain many useful examples, but it can be hard to find a useful example since there are over 40 TAUGs, and many TAUGs include examples that are useful outside a particular therapeutic area. The spreadsheet classifies TAUG examples by domain, so if a user has data that would be represented in a particular domain, the spreadsheet can identify TAUGs that might have examples relevant to their data.
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.
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.
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.
In all versions of the SDTMIG through v3.2, the "Conformance" section includes the following as a criterion for conformance:
If your data is based on a general observation class, you can determine the answer to this question by consulting the SDTM. Qualifiers for your general observation class, timing variables, and identifiers can generally be added to the dataset.
The earliest version of the SDTMIG had only one domain for tests on biologic specimens taken from a study subject, the Laboratory Test (LB) domain. SDTMIG v3.4 has 10 domains for specimen-based findings, plus the Biospecimen Events domain.
SDTM v2.0 is the first major release since the model's original publication in 2004. Among many modifications and updates, the most significant change is that variable metadata is represented in a new structure.
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.
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.
CDISC employs a rigorous approach to developing data standards. Each standard is informed and shaped by experts, making them not just of the highest quality, but also attuned to the practicalities of their implementation.