CDISC submission standard

1. CDISC submission standard • CDISC SDTMunfolding the core model that is the basis both for the specialised dataset templates (SDTM domains) optimised for medical reviewers • CDISC Define.xmlmetadata describing the data exchange structures (domains)

2. Background: CDISC SDTM’s fundamental model for organizing clinical data General classes Generic structure • Unique identifiers • Topic variable or parameter • Timing Variables • Qualifiers. Interventions Observation Findings SDTM Domains Subject Events (dataset structures) CM EX EG IE LB PE AE DS … The patient/subject focused information model of the clinical ‘reality’ (general classes of observations on subjects: interventions, findings, events). This model has been developed by CDISC/SDS team and exist today only as a text description.

3. CDISC SDTM’s Domains Interventions Events Findings Other Exposure AE Labs Incl Excl* Demog ConMeds Disposition Vitals Subj Char* RELATES* SUPPQUAL* Subst Use* MedHist PhysExam ECG Comments* Study Design* QS*, MB* CP*, DV* * New in Version 3 Study Sum* From CDISC SDTM Overview & Impact to AZ, 2004, by Dan Godoy, presented at the first CDISC/SDM meeting 20 October 2004

4. Basic Concepts in CDISC SDTMObservations and Variables • The SDTM provides a general framework for describing the organization of information collected during human and animal studies. • The model is built around the concept of observations, which consist of discrete pieces of information collected during a study. Observations normally correspond to rows in a dataset. • Each observation can be described by a series of named variables. Each variable, which normally corresponds to a column in a dataset, can be classified according to its Role. • Observations are reported in a series of domains, usually corresponding to data that were collected together. A domain is defined as a collection of observations with a topic-specific commonality about a subject. From the Study Data Tabulation Model document

5. Basic Concepts in CDISC/SDTMVariable Roles • A Role determines the type of information conveyed by the variable about each distinct observation and how it can be used. • A common set of Identifier variables, which identify the study, the subject (individual human or animal) involved in the study, 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), and vary according to the type of observation. • A common set of Timing variables, which describe the timing of an 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). The list of Qualifier variables included with a domain will vary considerably depending on the type of observation and the specific domain • Rule variables, which express an algorithm or executable method to define start, end, or looping conditions in the Trial Design model. From the Study Data Tabulation Model document

6. Example: Mapping Vital Signs From CDISC End to End Tutorial - DIA Amsterdam 7 Nov 2004, Pierre-Yves Lastic, Sanofi-Aventis and Philippe Verplancke, CRO24

7. CDISC’s Submission standard • Underlying Models:CDISC Study Data Tabulation Model • Clinical Observations • General Classes: Events, Findings, Interventions • Trial Design Model • Elements, Arms, Trial Summary Parameters etc. • Domains, submission dataset templates:CDISC SDTM Implementation Guide

8. CDISC SDTM fundamental model for organizing data collected in clinical trials Concept of Observations, which consist of discrete pieces of information collected during a study described by a series of named variables. General Classes of Observations: Events, Findings, Interventions Variable Roles: determines the type of information conveyed by the variable about each distinct observation: Topic variables, Identifier variables, Timing variables, Rule variables, and Qualifiers (Grouping, Result, Synonym, Record, Variable) General principles and standards

9. CDISC SDTM Domains SAS Dataset implementations(dataset templates)e.g. Vital Signs domains Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO8601 for dates/timings, and both Original & Standard values expected CDISC SDTM fundamental model for organizing data collected in clinical trials Concept of Observations, which consist of discrete pieces of information collected during a study described by a series of named variables. General Classes of Observations: Events, Findings, Interventions Variable Roles: determines the type of information conveyed by the variable about each distinct observation: Topic variables, Identifier variables, Timing variables, Rule variables, and Qualifiers (Grouping, Result, Synonym, Record, Variable) General principles and standards

10. CDISC SDTM Domains SAS Dataset implementations(dataset templates)e.g. Vital Signs domains Decoded format, that is, the textual interpretation of whichever code was selected from the code list. Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO8601 for dates/timings, and both Original & Standard values expected Identifiers of records per dataset and study CDISC SDTM fundamental model for organizing data collected in clinical trials Concept of Observations, which consist of discrete pieces of information collected during a study described by a series of named variables. General Classes of Observations: Events, Findings, Interventions Variable Roles: determines the type of information conveyed by the variable about each distinct observation: Topic variables, Identifier variables, Timing variables, Rule variables, and Qualifiers (Grouping, Result, Synonym, Record, Variable) General principles and standards

11. Controlled TerminologiesCT Packages for SDTM e.g. Codelist Patient Positiion and proposed terms for VSTESTCD CDISC SDTM Domains SAS Dataset implementations(dataset templates)e.g. Vital Signs domains Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO8601 for dates/timings, and both Original & Standard values expected CDISC SDTM fundamental model for organizing data collected in clinical trials Concept of Observations, which consist of discrete pieces of information collected during a study described by a series of named variables. General Classes of Observations: Events, Findings, Interventions Variable Roles: determines the type of information conveyed by the variable about each distinct observation: Topic variables, Identifier variables, Timing variables, Rule variables, and Qualifiers (Grouping, Result, Synonym, Record, Variable) General principles and standards

12. Controlled TerminologiesCT Packages for SDTM e.g. Codelist Patient Positiion and proposed terms for VSTESTCD CDISC SDTM Domains SAS Dataset implementations(dataset templates)e.g. Vital Signs domains Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO8601 for dates/timings, and both Original & Standard values expected CDISC SDTM fundamental model for organizing data collected in clinical trials Concept of Observations, which consist of discrete pieces of information collected during a study described by a series of named variables. General Classes of Observations: Events, Findings, Interventions Variable Roles: determines the type of information conveyed by the variable about each distinct observation: Topic variables, Identifier variables, Timing variables, Rule variables, and Qualifiers (Grouping, Result, Synonym, Record, Variable) General principles and standards

13. Controlled TerminologiesCT Packages for SDTM e.g. Codelist Patient Positiion and proposed terms for VSTESTCD CDISC SDTM Domains SAS Dataset implementations(dataset templates)e.g. Vital Signs domains Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO8601 for dates/timings, and both Original & Standard values expected CDISC SDTM fundamental model for organizing data collected in clinical trials Concept of Observations, which consist of discrete pieces of information collected during a study described by a series of named variables. General Classes of Observations: Events, Findings, Interventions Variable Roles: determines the type of information conveyed by the variable about each distinct observation: Topic variables, Identifier variables, Timing variables, Rule variables, and Qualifiers (Grouping, Result, Synonym, Record, Variable) General principles and standards define.xml Case Report Tabulation Data Definition Specificationto submit the Data Definition Document (submission dataset metadata) in a machine-readable format

14. CRTDDS = Case Report Tabulation Data Description Specification (= an ODM extension, formerly called „define.xml“) will replace define.pdf in e-CTD (in an item) Item Item ValueList Item ItemGroup ItemGroup Item ItemGroup Item Controlled TerminologiesCT Packages for SDTM e.g. Codelist Patient Positiion and proposed terms for VSTESTCD CDISC SDTM Domains SAS Dataset implementations(dataset templates)e.g. Vital Signs domains Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO8601 for dates/timings, and both Original & Standard values expected <ItemDef OID="SU.SUTRT.SMKCLASS" Name="SMKCLASS" DataType="integer" Length="8“ Origin="CRF Page" Comment="Substance Use CRF Page 4" def:Label="Smoking classification"> <CodeListRef CodeListOID="SMKCLAS" /> </ItemDef> <CodeList OID="SMKCLAS" Name="SMKCLAS" DataType="integer"> <CodeListItem CodedValue="1"> <Decode> <TranslatedText xml:lang="en">NEVER SMOKED</TranslatedText> </Decode> </CodeListItem> <CodeListItem CodedValue=“2"> <Decode> <TranslatedText xml:lang="en">SMOKER</TranslatedText> </Decode> </CodeListItem> <CodeListItem CodedValue=“3"> <Decode> <TranslatedText xml:lang="en">EX SMOKER</TranslatedText> </Decode> </CodeListItem> define.XML as machine-readable replacement for define.pdf (= prevoius called Data Defintion Tables in item 11) > Needs complete syntax to reference external lists From Randy Levins presentation, see http://www.cdisc.org/publications/interchange2005/session8/JANUS2005.pdf > And to reference sponsor defined code lists cross studies CDISC SDTM fundamental model for organizing data collected in clinical trials Concept of Observations, which consist of discrete pieces of information collected during a study described by a series of named variables. General Classes of Observations: Events, Findings, Interventions Variable Roles: determines the type of information conveyed by the variable about each distinct observation: Topic variables, Identifier variables, Timing variables, Rule variables, and Qualifiers (Grouping, Result, Synonym, Record, Variable) General principles and standards define.xml Case Report Tabulation Data Definition Specificationto submit the Data Definition Document (submission dataset metadata) in a machine-readable format

15. CDISC SDTM Domains SAS Dataset implementations(dataset templates)e.g. Vital Signs domains • SDTM fundemantal mode is also the basis for: • SEND Domains for Nonclinical Data (generated from animal toxicity studies) • Future domains of derived data, capturing metadata to describe derivations and analyses. Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO8601 for dates/timings, and both Original & Standard values expected CDISC SDTM fundamental model for organizing data collected in clinical trials Concept of Observations, which consist of discrete pieces of information collected during a study described by a series of named variables. General Classes of Observations: Events, Findings, Interventions Variable Roles: determines the type of information conveyed by the variable about each distinct observation: Topic variables, Identifier variables, Timing variables, Rule variables, and Qualifiers (Grouping, Result, Synonym, Record, Variable) General principles and standards

16. Basic Concepts in CDISC/SDTMSubclasses of Qualifiers • Grouping Qualifiers are used to group together a collection of observations within the same domain. • Examples include --CAT, --SCAT, --GRPID, --SPEC, --LOT, and --NAM. The latter three grouping qualifiers can be used to tie a set of observations to a common source (i.e., specimen, drug lot, or laboratory name, respectively). • 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, and --LOINC which is an equivalent term for a --TEST and --TESTCD. • 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. • 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, --ORNHI, and --ORNLO, all of which are variable qualifiers of --ORRES: and --DOSU, --DOSFRM, and --DOSFRQ, all of which are variable qualifiers of --DOSE. observation and is • 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 allother SAE flag variables in the AE domain; and --BLFL, --POS and --LOC. From the Study Data Tabulation Model document

17. Basic Concepts in CDISC/SDTMVariable Roles • Topic variableswhich specify the focus of the observation (such as the name of a lab test), and vary according to the type of observation. • Grouping qualifiersare used to group together a collection of observations within the same domain. • Examples include --CAT, --SCAT, --GRPID, --SPEC, --LOT, and --NAM. The latter three grouping qualifiers can be used to tie a set of observations to a common source (i.e., specimen, drug lot, or laboratory name, respectively) • Synonym Qualifiersspecify an alternative name for a particular variable inan observation. • Examples include --MODIFY and --DECOD, which are equivalent terms for a --TRT or --TERM topic variable,and --LOINC which is an equivalent term for a --TEST and --TESTCD. Qualifier variables Observation Record Topic GroupingQual SynonymQual From the Study Data Tabulation Model document

18. Basic Concepts in CDISC/SDTM Variable Roles • Identifier variableswhich identify the study, the subject (individual human or animal) involved in the study, the domain, and the sequence number of the record. • Timing variableswhich describe the timing of an observation (such as start date and end date). • Result Qualifiersdescribe the specific results associated with the topic variable for a finding. It is the answer to the question raised by the topic variable. Depending on the type of result (numeric or character) different variables are being used. Includes variables for both original (as supplied values) and for standardised values (for uniformity). • Examples include --ORRES,--STRESC, and --STRESN. Qualifier variables Observation Record ResultQual Topic Identifier Timing From the Study Data Tabulation Model document

19. Basic Concepts in CDISC/SDTM Variable Roles • Variable Qualifiersare 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, --ORNHI, and --ORNLO, all of which are variable qualifiers of --ORRES: and --DOSU, --DOSFRM, and --DOSFRQ, all of which are variable qualifiers of --DOSE. • Indictors where the results falls with respect to reference range Qualifier variables Observation Record ResultQual VariableQual Topic Identifier Timing From the Study Data Tabulation Model document

20. Basic Concepts in CDISC/SDTM Variable Roles • Record Qualifiersdefine additional attributes of the observation record as a whole (rather than describing a particular variable within a record). • Examples include --REASND, AESLIFE, and allother SAE flag variables in the AE domain; and --BLFL, --POS and --LOC. Qualifier variables Observation Record ResultQual VariableQual RecordQual Topic Identifier Timing From the Study Data Tabulation Model document

21. Basic Concepts in CDISC/SDTMSubclasses of Qualifiers • Topic variables • Identifier variables • Timing variables • Rule variables • Qualifier variables • Grouping Qualifiers • Result Qualifiers • Synonym Qualifiers • Record Qualifiers • Variable Qualifiers Observation Record ResultQual VariableQual RecordQual Topic Identifier Timing GroupingQual SynonymQual From the Study Data Tabulation Model document