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 SDTMs fundamental model for
organizing clinical data
Generic structure
General classes

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.
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CDISC SDTMs Domains
Interventions
Events
Findings
Other
Exposure
AE
Labs
Demog
Incl Excl
ConMeds
Disposition
Vitals
Subj Char
RELATES
SUPPQUAL
MedHist
ECG
PhysExam
Subst Use
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
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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
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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
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Example Mapping Vital Signs
From CDISC End to End Tutorial - DIA Amsterdam 7
Nov 2004, Pierre-Yves Lastic, Sanofi-Aventis and
Philippe Verplancke, CRO24
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CDISCs Submission standard

• Underlying ModelsCDISC Study Data Tabulation
  Model
• Clinical Observations
• General Classes Events, Findings, Interventions
• Trial Design Model
• Elements, Arms, Trial Summary Parameters etc.
• Domains, submission dataset templatesCDISC 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
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
ltItemDef OID"SU.SUTRT.SMKCLASS" Name"SMKCLASS"
DataType"integer" Length"8 Origin"CRF Page"
Comment"Substance Use CRF Page 4"
defLabel"Smoking classification"gt
ltCodeListRef CodeListOID"SMKCLAS" /gt
lt/ItemDefgt ltCodeList OID"SMKCLAS"
Name"SMKCLAS" DataType"integer"gt
ltCodeListItem CodedValue"1"gt ltDecodegt
ltTranslatedText xmllang"en"gtNEVER
SMOKEDlt/TranslatedTextgt lt/Decodegt
lt/CodeListItemgt ltCodeListItem CodedValue2"gt
ltDecodegt ltTranslatedText
xmllang"en"gtSMOKERlt/TranslatedTextgt
lt/Decodegt lt/CodeListItemgt ltCodeListItem
CodedValue3"gt ltDecodegt
ltTranslatedText xmllang"en"gtEX
SMOKERlt/TranslatedTextgt lt/Decodegt
lt/CodeListItemgt
define.XML as machine-readable replacement for
define.pdf ( prevoius called Data Defintion
Tables in item 11) gt Needs complete syntax to
reference external lists From Randy Levins
presentation, see http//www.cdisc.org/publication
s/interchange2005/session8/JANUS2005.pdf gt 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 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 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
Identifier
Timing
Topic
From the Study Data Tabulation Model document
19
Basic Concepts in CDISC/SDTM Variable Roles

• 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.
• Indictors where the results falls with respect to
  reference range

Qualifier variables
Observation Record
ResultQual
VariableQual
Identifier
Timing
Topic
From the Study Data Tabulation Model document
20
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
ResultQual
VariableQual
RecordQual
Identifier
Timing
Topic
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
RecordQual
ResultQual
VariableQual
Topic
Identifier
Timing
GroupingQual
SynonymQual
From the Study Data Tabulation Model document