We have compiled a number of frequently asked questions to answer your inquiries about Controlled Terminology.
CDISC has published the first of a new kind of QRS supplement to the SDTMIG, a supplement for an oncology response criterion, Response Evaluation Criteria in Solid Tumors Version 1.1 (RECIST 1.1).
This article provides information on the ISO international standard for country codes is ISO-3166, which provides several representations of names for countries (ISO-3166-1) and their subdivisions (ISO-3166-2).
For human clinical trials, there is a growing movement to replace the term "study subject" with a terminology that is more respectful and recognizes the agency of those who consent to be treated and have their data collected. The term "participant”, rather than "study subject", is now used in ICH E6 R3 guidelines. The TransCelerate Biopharma Common Protocol Template states that "participant" may be used in patient-facing documents.
The CDISC Analysis Data Model Implementation Guide (ADaMIG) provides several timing variables for modeling clinical trial designs in analysis datasets. APHASE, APERIOD, and ASPER can be used in conjunction with related treatment variables to meet a variety of analysis requirements, from single-period parallel studies to much more complicated situations involving multiple treatment periods and even different studies. The goal of this article is to provide guidelines for identifying when to use the different timing variables that are available. Additional examples can be found in the References and Resources listed at the end of the article.
This article aims to clarify and establish a shared understanding of the recommended CDISC approach for identifying CDISC Biomedical Concepts (BCs) within a defined group of interest, with particular emphasis on Questionnaires, Ratings, and Scales (QRS) instruments.
The SDTM Implementation Guide for Associated Persons (SDTMIG-AP) v1.0 provides guidance on how to include data collected about persons who are not the study subjects in SDTM. However, the ADaM Implementation Guide has not provided guidance regarding how to include data from SDTM AP-- domains in ADaM datasets if needed for analysis.
The ADaMIG provides multiple ways to categorize and flag values to be analyzed. These ways may seem overlapping, and the most appropriate set of variables for a particular analysis may not be obvious to the user. This article is intended to provide some clarity on how to use appropriate variables and how to select the appropriate analysis variables.
When development of the SDTM and SDTMIG started, SAS was used almost universally across the pharmaceutical industry and at the US FDA.