1、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 SDTMs fundamental model for organizi
2、ng clinical data,Observation,Generic structure,Unique identifiers,Topic variable or parameter,Timing Variables,Qualifiers.,Interventions,Findings,Events,General classes,Subject,CM,EX,EG,IE,LB,PE,AE,DS,SDTM Domains,(dataset structures),The patient/subject focused information model of the clinical rea
3、lity (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.,* New in Version 3,Interventions,Events,ConMeds,Exposure,AE,MedHist,Disposition,Findings,ECG,PhysExam,Labs,Vitals,Demog,Oth
4、er,Subj Char*,Subst Use*,Incl Excl*,RELATES*,SUPPQUAL*,Study Sum*,Study Design*,QS*, MB*,Comments*,CP*, DV*,CDISC SDTMs Domains,From CDISC SDTM Overview & Impact to AZ, 2004, by Dan Godoy, presented at the first CDISC/SDM meeting 20 October 2004,Basic Concepts in CDISC SDTM Observations and Variable
5、s,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
6、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
7、 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 conveyed by the variable about each distinct observation and how i
8、t 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 acc
9、ording 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 (su
10、ch 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
11、Design model.,From the Study Data Tabulation Model document,Example: Mapping Vital Signs,From CDISC End to End Tutorial - DIA Amsterdam 7 Nov 2004, Pierre-Yves Lastic, Sanofi-Aventis and Philippe Verplancke, CRO24,CDISCs Submission standard,Underlying Models: CDISC Study Data Tabulation Model Clinic
12、al 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 Observatio
13、ns, 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 v
14、ariables, Identifier variables, Timing variables, Rule variables, and Qualifiers (Grouping, Result, Synonym, Record, Variable) General principles and standards,CDISC SDTM fundamental model for organizing data collected in clinical trials Concept of Observations, which consist of discrete pieces of i
15、nformation 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 va
16、riables, 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 for Data Exchange per study and for Medical Reviewers to easier understand
17、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 descr
18、ibed 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 (
19、Grouping, Result, Synonym, Record, Variable) General principles and standards,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 su
20、ch as ISO8601 for dates/timings, and both Original & Standard values expected,Identifiers of records per dataset and study,Decoded format, that is, the textual interpretation of whichever code was selected from the code list.,CDISC SDTM fundamental model for organizing data collected in clinical tri
21、als 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 dist
22、inct observation: Topic variables, Identifier variables, Timing variables, Rule variables, and Qualifiers (Grouping, Result, Synonym, Record, Variable) General principles and standards,Optimisations for Data Exchange per study and for Medical Reviewers to easier understand data Specific principles a
23、nd standards such as ISO8601 for dates/timings, and both Original & Standard values expected,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,CDISC
24、 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: determ
25、ines 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,Optimisations for Data Exchange per study and
26、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 Domains SAS Dataset implementations (dataset templates) e.g. Vital Signs domains,Controlled Terminologies CT Packages for SDTM
27、e.g. Codelist Patient Positiion and proposed terms for VSTESTCD,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
28、 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 pr
29、inciples and standards,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 Domains SAS Dataset implementations (dataset templates) e.
30、g. Vital Signs domains,define.xml Case Report Tabulation Data Definition Specification to submit the Data Definition Document (submission dataset metadata) in a machine-readable format,Controlled Terminologies CT Packages for SDTM e.g. Codelist Patient Positiion and proposed terms for VSTESTCD,CDISC
31、 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: determ
32、ines 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,Optimisations for Data Exchange per study and
33、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 Domains SAS Dataset implementations (dataset templates) e.g. Vital Signs domains,define.xml Case Report Tabulation Data Definit
34、ion Specification to submit the Data Definition Document (submission dataset metadata) in a machine-readable format,Controlled Terminologies CT Packages for SDTM e.g. Codelist Patient Positiion and proposed terms for VSTESTCD, NEVER SMOKED SMOKER EX SMOKER ,define.XML as machine-readable replacement
35、 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 fund
36、amental 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 t
37、ype 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,Optimisations for Data Exchange per study and for Medica
38、l Reviewers to easier understand data Specific principles and standards such as ISO8601 for dates/timings, and both Original & Standard values expected,CDISC SDTM Domains SAS Dataset implementations (dataset templates) e.g. Vital Signs domains,SDTM fundemantal mode is also the basis for:SEND Domains
39、 for Nonclinical Data (generated from animal toxicity studies)Future domains of derived data, capturing metadata to describe derivations and analyses.,Basic Concepts in CDISC/SDTM Subclasses of Qualifiers,Grouping Qualifiers are used to group together a collection of observations within the same dom
40、ain. 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
41、 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 t
42、he 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 -ORN
43、LO, 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). E
44、xamples 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 of the observation (such as the name of a lab test), and var
45、y according to the type of observation.,From the Study Data Tabulation Model document,Topic,Grouping Qual,Synonym Qual,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 gro
46、uping 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
47、-TRT or -TERM topic variable, and -LOINC which is an equivalent term for a -TEST and -TESTCD.,Observation Record,Qualifier variables,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,
48、and the sequence number of the record. Timing variables which describe the timing of an observation (such as start date and end date).,From the Study Data Tabulation Model document,Identifier,Timing,Result Qualifiers describe the specific results associated with the topic variable for a finding. It
49、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,Qualifier variables,Topic,Result Qual,Basic Concepts in CDISC/SDTM Variable Roles,From the Study Data Tabulation Model document,Identifier,
