CDISC models implemented for standardization of data collection, tabulation, and creation of analysis datasets for each use case category are summarized in the table below.
| Category | Collection | Tabulation | Analysis |
|---|---|---|---|
| Product Description | Study Data Tabulation Model (SDTM) | ||
| Nonclinical | Study Data Tabulation Model (SDTM) | ||
| Product Impact on Individual Health | Clinical Data Acquisition Standards Harmonization (CDASH) | Study Data Tabulation Model (SDTM) | Analysis Data Model (ADaM) |
| Product Impact on Population Health | Analysis Data Model (ADaM) |
Data for
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.
Overview
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