Use of Fast Healthcare Interoperability Resources (FHIR) in the Generation of Real World Evidence (RWE) demonstrated that electronic CRF data could be populated by mapping FHIR resources to CDASH/SDTM variables. To grow the use of FHIR for eSource beyond pilot projects, existing standards and workflows must be adapted to enable repeatable and scalable processes.
There is a lot of interest in the clinical trial community to understand what information can be obtained from Electronic Health Records (EHRs) to support clinical trials. The use of FHIR has been endorsed by the Office of National Coordinator for Health Information Technology (ONC) and is widely being used by EHR vendors.
"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).
When development of the SDTM and SDTMIG started, SAS was in almost universal use in the pharmaceutical industry and at FDA.
SNOMED (short for SNOMED Clinical Terms or SNOMED CT) is a set of medical terms used widely in clinical practice. Some have asked why CDISC develops its own Controlled Terminology, rather than using SNOMED. There are a number of reasons why we develop terminology:
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?
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.
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.
CDASH and SDTM are each optimized for different purposes, and the philosophy behind each drives the design. SDTM represents cleaned, final CRF data organized in a predictable format that facilitates data transmission, review and reuse. CDASH collects the data in a user-friendly, EDC/CRF-friendly way that maximizes data quality and flows smoothly into SDTM.
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.