Organization of Data by Common Topics
All models implemented as part of this guide collect and represent data by common topics with:
- CDASH and SDTM grouping logically related data points in domains; and
- ADaM dataset design customizable for 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.
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Overview
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