The scientific subject matter of the data and related activities such as data collection, data tabulation, data analysis, and data exchange drive which standards to implement. Implementation of TIG standards starts with the selection of standards based on the nature of the data and activities to be supported. After standards are selected, it is then possible to determine how the data are collected, represented, or exchanged using the standards.
At the highest level, use cases addressed in the TIG are aligned with data-related activities supported by the standards. use cases inherent to studies of tobacco productsboth tobacco study use cases and by data related processes they support. In the TIG:
- Standards for Collection will guide development and use of case report forms (CRFs) by implementing the CDISC CDASH Model.
- Standards for Tabulation will guide organization of data collected, assigned, or derived for a study by implementing SDTM.
- Standards for Analysis will specify the principles to follow in the creation of analysis datasets and associated metadata by implementing the ADaM.
- Standards for Data Exchange will support sharing of structured data between parties and across different information systems by implementing specified standard specifications and resources.
Use cases, activities, and associated sets of standards in scope for this guide are shown in the table below.
Observations and Variables - SDTMIG v3.4 - Wiki (cdisc.org) The SDTMIG for Human Clinical Trials is based on the SDTM’s general framework for organizing clinical trial information that is to be submitted to regulatory authorities. The SDTM is built around the concept of observations collected about subjects who participated in a clinical study. Each observation can be described by a series of variables, corresponding to a row in a dataset.
Datasets and Domains - SDTMIG v3.4 - Wiki (cdisc.org)
A domain is defined as a collection of logically related observations with a common topic. The logic of the relationship may pertain to the scientific subject matter of the data or to its role in the trial.
Each domain dataset is distinguished by a unique, 2-character code that should be used consistently throughout the submission. This code, which is stored in the SDTM variable named DOMAIN, is used in 4 ways: as the dataset name, as the value of the DOMAIN variable in that dataset, as a prefix for most variable names in that dataset, and as a value in the RDOMAIN variable in relationship tables (see Section 8, Representing Relationships and Data).
Data represented in SDTM datasets include data as originally collected or received, data from the protocol, assigned data, and derived data.
Datasets and Domains - SENDIG v3.1.1 - Wiki (cdisc.org)
Test results, examinations, and observations for subjects in a nonclinical study are represented in a series of SEND domains. A domain is defined as a collection of logically related observations with a common topic. The logic of the relationship may pertain to the scientific subject matter of the data or to its role in the study.
Although the domain name is carefully selected, it is the structures and specifications within the domain that drive placement of data. It is important to note that the domain structure is only used for organizational purposes. The --TEST and --METHOD variable entries in the domain contribute to the identification of the test performed and the conditions under which the test was performed; the domain name or organization is not intended to imply any of this information.
Each domain dataset is distinguished by a unique, 2-character code that should be used consistently throughout the submission. This code, which is stored in the SDTM variable named DOMAIN, is used in 4 ways: as the dataset name, as the value of the DOMAIN variable in that dataset, as a prefix for most variable names in that dataset, and as a value in the RDOMAIN variable in relationship tables (see Section 8, Representing Relationships and Data).
Datasets and Domains - SENDIG v3.1.1 - Wiki (cdisc.org)
When determining which general-observation class domain model is appropriate for reporting specific observations, refer to the domain definition included in the Assumptions section for each domain model (see Section 6, Domain Models Based on the General Observation Classes).
What or who is a subject
Add language here
Determining Where Data Belong
Standards for collection, tabulation, and analysis collect and represent data by common topics with:
- CDASH and SDTM grouping logically related data in domains; and
- ADaM dataset design customizable per analysis requirements.
Domains
SDTM
Observations about study subjects are normally collected for all subjects in a series of domains. A domain is defined as a collection of logically related observations with a common topic. The logic of the relationship may pertain to the scientific subject matter of the data or to its role in the trial. Each domain is represented by a single dataset.
Each domain dataset is distinguished by a unique, 2-character code that should be used consistently throughout the submission. This code, which is stored in the SDTM variable named DOMAIN, is used in 4 ways: as the dataset name, as the value of the DOMAIN variable in that dataset, as a prefix for most variable names in that dataset, and as a value in the RDOMAIN variable in relationship tables (see Section 8, Representing Relationships and Data).
SEND
Aside from a limited number of special-purpose domains, all subject-level SDTM datasets are based on 1 of the 3 general observation classes. When faced with a set of data that were collected and that "go together" in some sense, the first step is to identify SDTM observations within the data and the general observation class of each observation. Once these observations are identified at a high level, 2 other tasks remain:
- Determining whether the relationships between these observations need to be represented using GRPID within a dataset, as described in Section 8.1, (SENDIG v3.1.1) Relating Groups of Records Within a Domain Using the --GRPID Variable, or using RELREC between datasets, as described in Section 8.3, (SENDIG v3.1.1) Supplemental Qualifiers - SUPP-- Datasets
- Placing all the data items in 1 of the identified general observation class records, or in a SUPP-- dataset, as described in Section 8.5, (SENDIG v3.1.1) Relating Findings To Multiple Subjects - Subject Pooling
In practice, considering the representation of relationships and placing individual data items may lead to reconsidering the identification of observations, so the whole process may require several iterations.
ADD MORE TEXT HERE
Overview
Content Tools