SDTM (Study Data Tabulation Model) defines a standard structure for human clinical trial (study) data tabulations and for nonclinical 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 for clinical trials and on July 5, 2011 for nonclinical studies. Eventually, all data submissions will be expected to conform to this format. As a result, clinical and nonclinical Data Managers will need to become proficient in the SDTM to prepare submissions and apply the SDTM structures, where appropriate, for operational data management.
Background 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:
Identifier variables, which identify the study, subject of the observation, the domain, and the sequence number of the record Topic variables, which specify the focus of the observation (such as the name of a lab test) Timing variables, which describe the timing of the observation (such as start date and end date) Qualifier variables, which include additional illustrative text, or numeric values that describe the results or additional traits of the observation (such as units or descriptive adjectives). 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:
Grouping Qualifiers are used to group together a collection of observations within the same domain. Examples include—CAT and—SCAT. Result Qualifiers describe the specific results associated with the topic variable for a finding. It is the answer to the question raised by the topic variable. Examples include—ORRES, --STRESC, and—STRESN. Many of the values in the DM domain are also classified as Result Qualifiers. Synonym Qualifiers specify an alternative name for a particular variable in an observation. Examples include—MODIFY and—DECOD, which are equivalent terms for a --TRT or—TERM topic variable, --TEST and—LOINC which are equivalent terms for a --TESTCD. Record Qualifiers define additional attributes of the observation record as a whole (rather than describing a particular variable within a record). Examples include—REASND, AESLIFE, and all other SAE (serious adverse event) flag variables in the AE domain; and—BLFL, --POS and—LOC, --SPEC, --LOT, --NAM. Variable Qualifiers are used to further modify or describe a specific variable within an observation and is only meaningful in the context of the variable they qualify. Examples include—ORRESU, --ORNRHI, and—ORNRLO, all of which are variable qualifiers of—ORRES, and—DOSU and—DOSFRM, all of which are variable qualifiers of—DOSE. 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.
Datasets and domains 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 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:
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