Скачать презентацию CDISC submission standard CDISC SDTM unfolding the Скачать презентацию CDISC submission standard CDISC SDTM unfolding the

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

Background: CDISC SDTM’s fundamental model for organizing clinical data General classes Generic structure • Background: CDISC SDTM’s fundamental model for organizing clinical data General classes Generic structure • Unique identifiers • Topic variable or parameter • Timing Variables • Qualifiers. Interventions Findings Events Observation Subject SDTM Domains (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.

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

Basic Concepts in CDISC SDTM Observations and Variables • The SDTM provides a general Basic Concepts in CDISC SDTM Observations 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

Basic Concepts in CDISC/SDTM Variable Roles • A Role determines the type of information Basic Concepts in CDISC/SDTM Variable 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

Example: Mapping Vital Signs From CDISC End to End Tutorial - DIA Amsterdam 7 Example: Mapping Vital Signs From CDISC End to End Tutorial - DIA Amsterdam 7 Nov 2004, Pierre-Yves Lastic, Sanofi -Aventis and Philippe Verplancke, CRO 24

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

CDISC SDTM fundamental model for organizing data collected in clinical trials Concept of Observations, 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

CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains Optimisations 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 ISO 8601 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

CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains Optimisations 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 Decoded format, that is, the textual interpretation of whichever code was selected from the code list. Identifiers of records per dataset and study Specific principles and standards such as ISO 8601 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

CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains Controlled CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains Controlled Terminologies CT Packages for SDTM e. g. Codelist Patient Positiion and proposed terms for VSTESTCD Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO 8601 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

CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains Controlled CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains Controlled Terminologies CT Packages for SDTM e. g. Codelist Patient Positiion and proposed terms for VSTESTCD Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO 8601 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

CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains Controlled CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains Controlled Terminologies CT Packages for SDTM e. g. Codelist Patient Positiion and proposed terms for VSTESTCD Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO 8601 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 Specification to submit the Data Definition Document (submission dataset metadata) in a machine-readable format

CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains CRTDDS CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains CRTDDS = Controlled Terminologies CT Packages for SDTM e. g. Codelist Patient Positiion and proposed terms for VSTESTCD Case Report Tabulation Data Description Specification (= an ODM extension, formerly Optimisations for Data Exchange per study and for Medical Reviewers called „define. xml“) will replace define. pdf in e-CTD to Value. List (in an item) Original & Standard values expected NEVER SMOKED Item. Group Item define. XML for organizing data collected in CDISC SDTM fundamental modelas machine-readable replacement for define. pdf clinical trials prevoius called Data Defintion Tables in item 11) (= Concept of Observations, which consist of discrete pieces of information SMOKER Item. Group collected during a> Needs complete syntax to reference external lists study described by a series of named variables. Item General Classes of Observations: Events, Findings, Interventions From Randy Levins presentation, see Variable Roles: determines the type of information conveyed by the variable about each distinct observation: Topic variables, Identifier http: //www. cdisc. org/publications/interchange 2005/s Item. Group Item variables, Timing ession 8/JANUS 2005. pdf variables, Rule variables, and Qualifiers (Grouping, EX SMOKER Result, Synonym, Record, Variable) General principles and standards > And to reference sponsor defined code lists cross studies define. xml Case Report Tabulation Data Definition Specification to submit the Data Definition Document (submission dataset metadata) in a machine-readable format

CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains SDTM CDISC SDTM Domains SAS Dataset implementations (dataset templates) e. g. Vital Signs domains SDTM fundemantal mode is also the basis for: Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles and standards such as ISO 8601 for dates/timings, and both Original & Standard values expected • SEND Domains for Nonclinical Data (generated from animal toxicity studies) • Future domains of derived data, capturing metadata to describe derivations and analyses. 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

Basic Concepts in CDISC/SDTM Subclasses of Qualifiers • Grouping Qualifiers are used to group Basic Concepts in CDISC/SDTM Subclasses of Qualifiers • Grouping Qualifiers are used to group together a collection of observations within the same domain. – • Synonym Qualifiers specify an alternative name for a particular variable in an observation. – • 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 --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 --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). 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

Basic Concepts in CDISC/SDTM Variable Roles • • Topic variables which specify the focus Basic Concepts in CDISC/SDTM Variable Roles • • 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. 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. – Observation Record Topic Grouping Synonym Qual From the Study Data Tabulation Model document Examples include --MODIFY and --DECOD, which are equivalent terms for a --TRT or --TERM topic variable, Qualifier and --LOINC which is an equivalent term for variables a --TEST and --TESTCD.

Basic Concepts in CDISC/SDTM Variable Roles • Identifier variables • which identify the study, Basic Concepts in CDISC/SDTM Variable Roles • 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. • Timing variables which describe the timing of an observation (such as start date and end date). describe 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. Observation Record Topic Result Qualifiers Identifier Timing From the Study Data Tabulation Model document Result Qualifier variables

Basic Concepts in CDISC/SDTM Variable Roles • are used to further modify or describe Basic Concepts in CDISC/SDTM Variable Roles • 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. – Indictors where the results falls with respect Qualifier to reference range Observation Record Topic Variable Qualifiers variables Identifier Timing From the Study Data Tabulation Model document Result Qual Variable Qual

Basic Concepts in CDISC/SDTM Variable Roles • Record Qualifiers define additional attributes of the Basic Concepts in CDISC/SDTM Variable Roles • 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. Qualifier variables Observation Record Topic Identifier Timing From the Study Data Tabulation Model document Result Qual Variable Qual Record Qual

Basic Concepts in CDISC/SDTM Subclasses of Qualifiers • • Topic variables Identifier variables Timing Basic Concepts in CDISC/SDTM Subclasses of Qualifiers • • Topic variables Identifier variables Timing variables Rule variables • Qualifier variables – – – Grouping Qualifiers Result Qualifiers Synonym Qualifiers Record Qualifiers Variable Qualifiers Observation Record Topic Identifier Timing Grouping Synonym Qual From the Study Data Tabulation Model document Result Qual Variable Qual Record Qual