SDTM (Study Data Tabulation Model) defines a standard structure for human clinical trial (study) data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA). The Submission Data Standards team of Clinical Data Interchange Standards Consortium (CDISC) defines SDTM.
On July 21, 2004, SDTM was selected as the standard specification for submitting tabulation data to the FDA. Eventually, all data submissions will be expected to conform to this format. As a result, Clinical Data Managers will need to become proficient in the SDTM to prepare submissions and apply the SDTM structures, where appropriate, for operational data management.
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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 or table. Each variable can be classified according to its Role. A Role determines the type of information conveyed by the variable about each distinct observation and how it can be used. Variables can be classified into four major roles:
A fifth type of variable role, Rule, can express an algorithm or executable method to define start, end, or looping conditions in the Trial Design model.
The set of Qualifier variables can be further categorized into five sub-classes:
For example, in the observation, 'Subject 101 had mild nausea starting on Study Day 6,' the Topic variable value is the term for the adverse event, 'NAUSEA'. The Identifier variable is the subject identifier, '101'. The Timing variable is the study day of the start of the event, which captures the information, 'starting on Study Day 6', while an example of a Record Qualifier is the severity, the value for which is 'MILD'.
Additional Timing and Qualifier variables could be included to provide the necessary detail to adequately describe an observation.
Observations are normally collected for all subjects in a series of domains. A domain is defined as a collection of logically-related observations with a topic-specific commonality about the subjects in the trial. The logic of the relationship may relate to the scientific subject matter of the data, or to its role in the trial.
Typically, each domain is represented by a dataset, but it is possible to have information relevant to the same topicality spread among multiple datasets. Each dataset is distinguished by a unique, two-character DOMAIN code that should be used consistently throughout the submission. This DOMAIN code is used in the dataset name, the value of the DOMAIN variable within that dataset, and as a prefix for most variable names in the dataset.
The dataset structure for observations is a flat file representing a table with one or more rows and columns. Normally, one dataset is submitted for each domain. Each row of the dataset represents a single observation and each column represents one of the variables. Each dataset or table is accompanied by metadata definitions that provide information about the variables used in the dataset. The metadata are described in a data definition document named 'Define' that is submitted along with the data to regulatory authorities.
Submission Metadata Model uses seven distinct metadata attributes to be defined for each dataset variable in the metadata definition document:
Data stored in dataset variables include both raw (as originally collected) and derived values (e.g., converted into standard units, or computed on the basis of multiple values, such as an average). In SDTM only the name, label, and type are listed with a set of CDISC guidelines that provide a general description for each variable used by a general observation class.
Comments are included as necessary according to the needs of individual studies. The presence of an asterisk (*) in the 'Controlled Terms or Format' column indicates that a discrete set of values (controlled terminology) is expected to be made available for this variable. This set of values may be sponsor-defined in cases where standard vocabularies have not yet been defined (represented by a single *) or from an external published source such as MedDRA (represented by **).
The CDISC V3.x Submission Data Domain Models include special-purpose domains with a specific structure and cannot be extended with any additional qualifier or timing variables other than those specified.
Additional fixed structure, non-extensible special-purpose domains are discussed in the Trial Design model.
Most observations collected during the study (other than those represented in special purpose domains) should be divided among three general observation classes: Interventions, Events, or Findings:
In most cases, the identification of the general class appropriate to a specific collection of data by topicality is straightforward. Often the Findings general class is the best choice for general observational data collected as measurements or responses to questions. In cases when the topicality may not be as clear, the choice of class may be based more on the scientific intent of the protocol or analysis plan or the data structure.
All datasets based on any of the general observation classes share a set of common Identifier variables and Timing variables. Three general rules apply when determining which variables to include in a domain:
Special-Purpose Domains:
Interventions:
Events:
Findings:
Trial Design Domains:
Special-Purpose Relationship Datasets: