Page History

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 standards in this guide starts with the selection of standards determining which data standards should be used based on the nature of the data and activities to be supported. After standards are selectedan applicable set or sets of data standards have been identified, it is then possible to determine how the data are collected, represented, or exchanged using the standards. 

Standards Sets of data standards in this guide are organized by aligned with both use cases and activities. Selection of standards may begin with Given this, determining which standards to use may begin by selecting standards for the use case and activity to be addressed and activities to be supported. Use cases and activities with corresponding sections to describe guide implementation of standards are shown below where:supported. All TIG guidance—both general and detailed—should be reviewed prior to implementing standards. The following table presents use cases, activities, and corresponding sections in this guide that provide detailed instructions for implementation. Detailed instructions referenced include:

• Section 2.7, Standards for Collection, which guides development and use of CRFs Standards for Collection guide development and use of case report forms (CRFs) by implementing the CDISC CDASH Model
• Section 2.8, Standards for Tabulation guide , which guides organization of data collected, assigned, or derived for a study by implementing SDTM
• Section 2.9, Standards for Analysis specify , which specifies the principles to follow in the creation of creating analysis datasets and associated metadata by implementing the ADaM
• Section 2.10, Standards for Data Exchange support , which supports sharing of structured data between parties and across different information systems by implementing specified standard specifications standards and resources. 

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:  

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)

Once applicable standards are determined based on the use case and activity, the 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.

, its role, and analysis needs will determine where data belong (i.e., how the data are collected, represented, or exchanged using the standards). Standards for collection and tabulation in this guide collect and represent data using groupings of logically related data called domains. Domains are aligned between collection and tabulation standards to facilitate the transition of collected observations to their representation in tabulation datasets. Standards for analysis are organized in relation to analysis requirements, with the structure of tabulation datasets facilitating the generation of analysis datasets.

To use standards for collection and tabulation, compare the nature or role of the data to the scope of a domain. Domain names provide short descriptions of intended scope and may be used to narrow down which domains to consider. A domain standard may be used when the nature of the data and the domain scope are aligned.Observations will be collected using standardized collection fields when applicable and represented as rows in tabulation datasets. Each observation is described by a series of data points, which correspond to applicable data collection fields and variables in a tabulation dataset. A data collection field and/or tabulation variable may be used when the subject matter of a data point and the scope of a field and/or variable are aligned. The majority of data for a submission will be in scope for domains based on the General Observation Classes and a subset of Special Purpose domains described in the SDTM. Given this, referring to both the CDASH Model when applicable and the SDTM is highly recommended when using domains to support understanding of intended scope and to inform extensions and creation of custom domains when needed.

The design of analysis datasets is generally driven by the scientific and medical objectives of the study. A fundamental principle is that the structure and content of analysis datasets must support clear, unambiguous communication of the scientific and statistical aspects of the study. The purpose of ADaM is to provide a framework that both enables analysis of the data and allows reviewers and other recipients of the data to have a clear understanding of the data’s lineage from collection to analysis to results. ADaM provides the core and defines the spirit and intent of its concepts and standards. The model outlines the fundamental principles to follow in constructing analysis datasets and related metadata. Four types of metadata—analysis dataset metadata, analysis variable metadata, analysis parameter value-level metadata, and analysis results metadata—are described in ADaM. To establish which components are required in a submission, review current relevant files provided by the agency to which the submission is being sent. Other relevant documentation might include the study protocol, the statistical analysis plan (SAP), mock shells that define desired outputs, and any dataset specifications that have been defined.

Standards for data exchange are applicable to all use cases and support sharing of standard CRFs developed using collection standards, tabulation datasets generated using tabulation standards, and analysis datasets designed using analysis standards.ADD MORE TEXT HERE