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.


"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).

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.

For an implementer trying to decide where data belong in SDTM-based datasets, it's pretty clear when data belongs in a trial design dataset, a relationship dataset, one of the new study reference datasets, or one of the special purpose domains. However, it can be difficult to choose the right general observation class dataset, especially if data are about findings.

Standard(s): SDTM, SDTMIG

The International System of Units (SI), commonly known as the metric system, is the international standard for measurement. According to the National Institute of Science and Technology (NIST), the SI rests on a foundation of seven defining constants: the cesium hyperfine splitting frequency, the speed of light in vacuum, the Planck constant, the elementary charge (i.e., the charge on a proton), the Boltzmann constant, the Avogadro constant, and the luminous efficacy of a specified monochromatic source.

Standard(s): LAB, SDTMIG

In the diagrams below, the red line represents a graph of severity over time for a hypothetical event. For most adverse events, severity cannot be measured on a continuous scale; this line represents hypothetical actual severity, not data that could be recorded. The horizontal lines divide severity into the three categories, "Mild", "Moderate", and "Severe", which are used to describe adverse event severity.

Standard(s): CDASH, SDTM, SDTMIG

When development of the SDTM and SDTMIG started, SAS was in almost universal use in the pharmaceutical industry and at FDA.

On occasion the mapping from CDASH to SDTM is complex. This article provides a step-by-step explanation to help follow the iteration from the CDASH example to the SDTM example.

Standard(s): CDASH, Dataset-XML, Define-XML, SDTM

The QNAM values that appear in various examples published in the SDTMIG and TAUGs have sometimes included the domain code, and sometimes not.

Standard(s): SDTM, SDTMIG

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.