Transcript CDISC submission standard • CDISC SDTM unfolding the core model that is the basis both for the specialised dataset templates (SDTM domains) optimised for.

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
•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
ConMeds
Disposition
Vitals
Subj Char*
RELATES*
SUPPQUAL*
Subst Use*
* New in Version 3
MedHist
PhysExam
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 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 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 Nov 2004, Pierre-Yves Lastic, SanofiAventis and Philippe Verplancke, CRO24
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
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 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
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 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
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 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
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 Codelist Specification
CDISC Codelist Values
Codelist_Nam e
Codelist_Nam e
VSTEST
Codelist_Label
Vital Signs
Test Name
VSTEST
Optimisations for Data
Exchange
per
Upper_Case
Y
VSTEST
study and for Medical Reviewers to
Restriction_8char
Y
VSTEST
easier understand data
Extensible_NY
Y
VSTEST
Reference_Description
Organization
Name:
CDISC
VSTEST
Specific principles and
standards
such
Document Title:
Study
Data Tabulation Model
as ISO8601 for dates/timings,
and
both
Implementation Guide: Human Clinical Trials
Original & Standard values
expected
Document Version: 1.01
Date: 2004-07-14
CDISC Codelist Specification
Chapter:10.3.3 Vital Signs Test Codes CDISC Codelist Values
Page:
169
Codelist_Nam e
VSRESU
Codelist_Nam e
Controlled Term s
Reference_URL
VSTEST
http://www.cdisc.org/models/sds/v3.1/index.html
Codelist_Label
Units for Vital Signs Results
VSRESU
Controlled Term s
--TESTCD
WEIGHT
HEIGHT
HR
PULSE
SYSBP
--TEST
Weight
Height
Heart Rate
Pulse Rate
Systolic Blood Pressure
Com m ent
DIABP
Diastolic Blood Pressure
Com m ent
kg
Upper_Case
N
VSRESU
lb
VSTEST
RESP
Respiratory Rate
Restriction_8char
N
VSRESU
cm
VSTEST
TEMP
Temperature
CDISC
Codelist
Specification
CDISC
Codelist
CDISC
SDTM
fundamental
model
for
organizing
data
collected
in Values
Extensible_NY
Y
VSRESU
in
VSTEST
FRMSIZE
Frame Size
SIZECD
Codelist_Name
RACE
Codelist
Controlled
Term s
clinical
trials
Reference_Description
Not applicable
VSRESU
mmHG
VSTEST
BMI
Body Mass Index
Reference_URL
VSRESU
Not applicableConcept
beats/min
Codelist_Label
Race
RACE of AMERICAN
INDIAN OR
ALASKA NATIVE
VSTEST
Body Surface
Area
of Observations,
which consist
of discrete BSA
pieces
information
VSRESU
kg/m2
VSTEST
BODYFAT
Body Fat
collected
study described by a series of named
variables.
Upper_Case
Y during a VSRESU
RACE
ASIAN
VSTEST
MAP
Mean Arterial Pressure
m2
VSRESU
General
Observations:CEvents, Findings,
Interventions
Restriction_8char
N Classes of
RACE
BLACK OR AFRICAN AMERICAN
VSRESU
F
Variable
the type of information conveyed
by theHAWAIIAN OR OTHER PACIFIC ISLANDER
Extensible_NY
N Roles: determines
RACE
NATIVE
VSRESU
breath/min
variable about each
distinct
observation:
Topic
variables,
Identifier
VSRESU
g
Reference_Description variables,
Organization
FDA
RACE (Grouping,
WHITE
TimingName:
variables,
Rule variables, and Qualifiers
VSRESU
% Collection of
Document Title: Draft
Guidance for Industry
Result, Synonym, VSRESU
Record, Variable)
Race and Ethnicity Data in Clinical Trials
Ohm
Date:
January 2003
General
principles
and standards
Chapter: III COLLECTING RACE AND ETHNICITY DATA IN
CLINICAL TRIALS
Codelist_Name: Race
Page: 5
Reference_URL
http://www.fda.gov/cder/guidance/5054dft.pdf
Com m ent
FDA code
FDA code
FDA code
FDA code
FDA code
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 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 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
=
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
„define.xml“) will replace define.pdf in e-CTD
study and for Medical Reviewerscalled
to
ValueList (in an item)
easier
<ItemDef
understand
OID="SU.SUTRT.SMKCLASS"
data
Name="SMKCLASS" DataType="integer" Length="8“
Item
Item
Page" such
Comment="Substance Use CRF Page 4" def:Label="Smoking classification">
SpecificOrigin="CRF
principles and standards
<CodeListRef
CodeListOID="SMKCLAS"
/>
as ISO8601 for dates/timings, and both
</ItemDef>
Original
& Standard values expected
<CodeList OID="SMKCLAS" Name="SMKCLAS" DataType="integer">
<CodeListItem CodedValue="1">
<Decode>
<TranslatedText xml:lang="en">NEVER SMOKED</TranslatedText>
ItemGroup
Item
</Decode>
define.XML
for define.pdf
modelas
for machine-readable
organizing data collectedreplacement
in
</CodeListItem> CDISC SDTM fundamental
clinical
trials
<CodeListItem CodedValue=“2">(= prevoius called Data Defintion Tables in item 11)
<Decode>
Concept of Observations, which consist of discrete pieces of information
<TranslatedText
xml:lang="en">SMOKER</TranslatedText>
ItemGroup
Item external lists
collected
during a>study
described
by a seriessyntax
of namedto
variables.
Needs
complete
reference
</Decode>
General Classes of Observations: Events, Findings, Interventions
</CodeListItem>
From Randy Levins presentation, see
Variable Roles: determines the type of information conveyed by the
<CodeListItem CodedValue=“3">
variable about each
distinct observation: Topic variables, Identifier
http://www.cdisc.org/publications/interchange2005/se
<Decode>
ItemGroup
Item
variables,
Timing
variables,
Rule
variables, and Qualifiers (Grouping,
<TranslatedText xml:lang="en">EX
SMOKER</TranslatedText>
ssion8/JANUS2005.pdf
Result, Synonym, Record, Variable)
</Decode>
</CodeListItem> 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 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 ISO8601 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 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 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
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, 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).
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. 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
Identifier
Timing
From the Study Data Tabulation Model document
Result
Qual
Qualifier
variables
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
Qualifier
to reference range
Observation
Record
Topic
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 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 variables
Rule variables
•
Qualifier variables
–
–
–
–
–
Grouping Qualifiers
Result Qualifiers
Synonym Qualifiers
Record Qualifiers
Variable Qualifiers
Observation Record
Topic
Identifier
Timing
Grouping Synonym
Qual
Qual
From the Study Data Tabulation Model document
Result
Qual
Variable
Qual
Record
Qual