understanding the potential of share
TRANSCRIPT
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InnovionUnderstanding the potential of Share: a visual business case and technical use casePeter Van Reusel, CDISC E3C Chair
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Agenda
Introduction
Specify by Browsing
Metadata-driven Process
Call to Action
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Agenda
Introduction
Specify by Browsing
Metadata-driven Process
Call to Action
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Provides input to the operational strategy and feasibility of clinical research studiesParticipates in defining the key components of the clinical protocolsData review, analysis and interpretationAnalyze emerging safety profile of the drugEnsure appropriateness of the chosen subject populationAssesses the performance of techniques used for endpoint measures
Establishing an objective framework for conducting an investigationPlacing data and theory on an equal scientific footingDesigning data production through experimentationQuantifying the influence of chanceEstimating systematic and random effectsCombining theory and data using formal methods
The Responsibilities ofClinicians & Statisticians
DataStandardsExperts!
!Clinicians
Statisticians
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Updates over time (MAY 2016)
BRIDGv3.0
BRIDGv2.2
ADaMv2.1
ADaM IGv1.0
BRIDGv1.1.1
BRIDGv2.0
BRIDGv2.1
SDTMv1.2
SDTM IGv3.1.2
CDASHv1.0
ODMv1.1
SDTMv1.0
SDTM IGv3.1
ODMv1.2
BRIDGv3.0.1
BRIDGv3.0.2
BRIDGv3.0.3
ProtocolModelv1.0
ADaM Val.
Checksv1.0
ODMv1.3.1
SDTMv1.1
SDTM IGv3.1.1
ODMv1.2.1
Define.xmlv1.0
ADaMv2.0
ODMv1.3
20092002 2010200820052003 2004
/
Content Standards
Technical Standards
Semantics
Therapeutic Areas
2006 2007
BRIDGv1.0
BRIDGv1.1
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Updates over time (MAY 2016)
BRIDGv3.0.1
BRIDGv3.0.2
BRIDGv3.0.3
ProtocolModelv1.0
ADaM Val.
Checksv1.0
ODMv1.3.1
20122011 20132010
Alzheimerv1.0
SENDv3.0
SDTMv3.1.2Am.1
ADaM Val.
Checksv1.1
CDASHv1.1
SDM.XMLv1.0
Pain
Tuberculosis
Devices
BRIDGv3.1
BRIDGv3.2
PRM Toolsetv1.0
CDASH UGv1.0
SDTM v1.3SDTM IG
v3.1.3
ADaMVal. Checks
v1.2
Parkinson’sDisease
Virology v1.0
2014
SDTMv1.4
SDTM IGv3.2
Alzheimerv1.1
Asthma
PKD
Define.xml v2.0
CDASH v1.2
ADaM MD Guide
CDASH E2BSAE IG
SDTM Associated Persons IG
v1.0
ODMv1.3.2
2015
Content Standards
Technical Standards
Semantics
Therapeutic Areas
2016
SEND IG v3.1
ProtocolConceptGuide
ADaM IG v1.1
Multiple Sclerosis
DiabetesQT Studies
Cardiovascular
Influenza
BRIDG v4.0
Dataset-xml v1.0
ADaM IG v2.0
CDASH v2.0
Breast cancer v1.0
Traumatic Brain Injury
COPD
CTR-XML v1.0
CDASH v1.2
PROTOCOL.XML1.0
Diabetic Kidney Disease v1.0
Define.xml IG Validation
SEND IG v3.1
SEND DART V1.0
Define.xmlv2.1
ADaM IG v1.2ADaM IG 2.0
SDTM IG v3.4 Batch 1
BRIDG UG v2
SDTM Device IG v2.0
Dyslepidemia
Schizophrenia
ADaM OCCDS V1.0ADaM Validation
Checks V1.3
Analysis Results Metadata v1.0
Hepatitis C
SDTM Pharmacogenomics
IG v1.0
SDTM DeviceSubmission Pilot
SDTM v1.5SDTM IG v3.3
RDF UG v1.0
RDF RG v1.0
Virology v2.0
…
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Overview of CDISC Models (JUL 2016)
Content Standards
Technical Standards
Semantics
Therapeutic Areas
UpcomingPROTOCOL DEFINE.XML ODM THERAPEUTICAREAS
ADaMSDTMCDASH
BRIDG
SEND
SHARE
XML TECH
PROTOCOL v1.0 (JAN 2010)PRM TOOLSET v1.0 (APR 2012)PROTOCOL CONCEPTS GUIDE (AUG 2014)
CDASH v1.0 (OCT 2008)CDASH v1.1 (JAN2011)CDASH UG v1.0 (APR 2012)CDASH E2B SAE IG (NOV 2013)CDASH v1.2 (AUG 2014)CDASH v2.0 (DEC 2014)
BRIDG v1.0 (JUN 2007)BRIDG v1.1 (OCT 2007)BRIDG v1.1.1 (FEB 2008)BRIDG v2.0 (JUN 2008)BRIDG v2.1 (OCT 2008)BRIDG v2.2 (MAY 2009)BRIDG v3.0 (OCT 2009)BRIDG v3.0.1 (FEB 2010)BRIDG v3.0.2 (AUG 2010)BRIDG v3.0.3 (DEC 2010)BRIDG v3.1 (FEB 2012)BRIDG v3.2 (SEP 2012)BRIDG v4.0 (NOV 2014)BRIDG UG v2.0 (MAR 2015)
SDTM v1.0 + SDTM IG v3.1(JUL 2004)SDTM v1.1 + SDTM IG v3.1.1(DEC2014)SDTM v1.2 + SDTM IG v3.1.2(NOV 2008)SDTM IG v3.1.2 + AMENDMENT 1(DEC 2011)SDTM MEDICAL DEVICES v1.0(DEC 2012)SDTM ASSOCIATED PERSONS IG v1.0 (DEC 2013)SDTM v1.4 + SDTM IG v3.2(DEC 2013)SDTM QRS SUPPLEMENTS (PERIODIC)SDTM PHARMACOGENOMICS IG v1.0 (MAY 2015)
DEFINE.XML v1.0 (FEB 2005)DEFINE.XML v2.0 (MAR 2013)DATASET-XML v1 (APR 2014)CTR-XML v1.0 (MAR 2016)DEFINE.XML IG VALIDATION(NOV 2014)DEFINE.XML v2.1 (2016)
SDTM v1.5 + SDTM IG v3.3(MAY 2014 – DEC 2015)SDTM DEVICE IG v2.0(FEB 2015)SDTM IG v3.4 Batch 1 (Q1 2016)
SDM.XML v1.0 (SEP 2011)RDF RG v1.0 (JUN 2015)RDF UG v1.0 (JUL 2015)PROTOCOL.XML (Q3 2016)
ODM v1.1 (APR 2002)ODM v1.2 (JAN 2004)ODM v1.2.1 (JAN 2005)ODM v1.3 (DEC 2006)ODM v1.3.1 (FEB 2012)ODM v1.3.2 (MAR 2013)
ADaM v2.0 (AUG 2006)ADaM v2.21 + ADaM IG(DEC 2009)ADaM VALIDATION CHECKSv1.0 (SEP 2010)ADaM VALIDATION CHECKSv1.1 (JUL 2011)ADaM VALIDATION CHECKSv1.2 (JUL 2012)Analysis Results Metadata v1.0(JAN 2015)ADaM OCCDS v1.0 (JUL 2015)ADaM VALIDATION CHECKSv1.3 (MAR 2015)ADaM IG v1.1 (FEB 2016)
SEND IG v3.0 (MAY 2011)SEND IG v3.1 (JUL 2016)SEND DART v1.0 AUG 2016
CDISC SHARE
HEALTHCARE LINK
ALZHEIMER v1.0 (SEP 2011)PAIN (JUN 2012)TUBERCULOSIS (JUN 2012)PARKINSON’S DISEASE(OCT 2012)VIROLOGY v1.0 (NOV 2012)DEVICES (DEC 2012)PKD (FEB 2013)ALZHEIMER v2.0ASTHMA v1.0MULTIPLE SCLEROSIS(MAY 2014)DIABETES (AUG 2014)CARDIOVASCULAR v1.0 (OCT 2014)INFLUENZA v1.0 (NOV 2014)QT STUDIES v1.0 (DEC 2014)HEPATITIS C v1.0 (MAY 2015)DISLIPIDEMIA v1.0 (JUN 2015)SCHIZOPHRENIA v1.0 (JUN 2015)VIROLOGY v2.0 (SEP 2015)TRAUMATIC BRAIN INJURY v1.0(DEC 2015)ADaM SUPPLEMENT TO DIABETES(DEC 2015)COPD v1.0 (JAN 2016)TUBERCULOSIS v2.0 (FEB 2016)BREAST CANCER v1.0 (MAY 2016)
ADaM IG v1.2 (Q1 2016)ADaM IG v2.0 (Q4 2016)
SHARE API PILOT (Q2 2016)
DIABETIC KIDNEY DISEASE v1.0(Q1 2016)RHEUMATOID ARTHRITIS (Q2 2016)CV IMAGING v1.0 (Q2 2016)PROSTATE CANCER v1.0(Q3 2016)MAJOR DEPRESSIVE DISORDER(Q3 2016)GENERAL ANXIETY DISORDER v1.0(Q3 2016)BI-POLAR DISORDER v1.0 (Q3 2016)SOLID ORGAN (KIDNEY) TRANSPLANT (Q3 2016)
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Agenda
Introduction
Specify by Browsing
Metadata-driven Process
Call to Action
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Visualizing Metadata Today
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Analysis Results
Visualizing Metadata Today
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Analysis Dataset
Visualizing Metadata Today
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Visualizing Metadata Today
ADaM Define.xml
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Visualizing Metadata Today
SDTM Define.xml and aCRF
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Current MDRs represent metadata in a tabular format
Current Metadata Repository
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Current MDRs represent metadata in a tabular format
Current Metadata Repository
Clunky
No Benefits
Confusing
Boring
Not userfriendly
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DISCLAIMER NOTEThe following is not a software demonstration
Sole purpose is to illustrate
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AEAdverseEvents
EXExposure
CMConcomittantMedication
MHMedicalHistory
VSVital Signs
ListingsTables Figures
LBLaboratoryTest Results
Enter your search here
SDTMDOMAINSDTM VariableValue Level MetadataControlled TerminologyComputational algorithm
ADaMDOMAINADaM VariableADaM ParametersControlled TerminologyComputational algorithm
CDASHDOMAINCDASH VariableValue Level MetadataControlled Terminology
DATACOLLECTION
ANALYSISSHOPPING CART
End Points
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SDTMDOMAINSDTM VariableValue Level MetadataControlled TerminologyComputational algorithm
ADaMDOMAINADaM VariableADaM ParametersControlled TerminologyComputational algorithm
CDASHDOMAINCDASH VariableValue Level MetadataControlled Terminology
Graphical Approaches to the Analysis of Safety Data from Clinical Trials”. Amit, et. al.
From “Graphical Approaches to the Analysis of Safety Data from Clinical Trials”. Amit, et. al.
Figures
x
Distribution of maximum LFT values by treatment.
Panel of LFT shift from baseline to maximum by treatment
LFT Patient profiles
Most Frequent On Therapy Adverse Events
Mean Change from Baseline in QTc by time and treatment.
Distribution of ASAT by time and treatment
Cumulative distribution (with SEs) of time to first AE of special interest
Enter your search here
DATACOLLECTION
ANALYSISSHOPPING CART
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SAMPLE TEMPLATE
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AEAdverseEvents
EXExposure
CMConcomittantMedication
MHMedicalHistory
VSVital Signs
ListingsTables Figures
LBLaboratoryTest Results
SDTMDOMAINSDTM VariableValue Level MetadataControlled TerminologyComputational algorithm
ADaMDOMAINADaM VariableADaM ParametersControlled TerminologyComputational algorithm
CDASHDOMAINCDASH VariableValue Level MetadataControlled Terminology
Enter your search here
DATACOLLECTION
ANALYSISSHOPPING CART
End Points
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SDTMDOMAINSDTM VariableValue Level MetadataControlled TerminologyComputational algorithm
ADaMDOMAINADaM VariableADaM ParametersControlled TerminologyComputational algorithm
CDASHDOMAINCDASH VariableValue Level MetadataControlled Terminology Listings
Listing 2.4 Current Cancer History – All Treated Patients Experiencing Critical Events
Listing 2.5 Prior and Concomitant Medication – All Treated Patients Experiencing Critical Events
x
Listing 4.1 Adverse Event Listing. All Pre-Treatment Adverse Events – All Treated Patients Experiencing …
Listing 4.2 Adverse Event Listing. Treatment Emergent Adverse Events – All Treated Patients Experiencing …
Listing 4.3 Adverse Event Listing. Serious Treatment Emergent Adverse Events – All Treated Patients ..
Listing 4.4 Adverse Event Listing. Serious Treatment Emergent Adverse Events Related To Study Drug …
Listing 2.6 Physical Examination at Screening – All Treated Patients Experiencing Critical Events
Listing 3.1 Reference Chemotherapy and Concomitant Chemotherapies – All Treated Patients Experiencing ..
Listing 4.5 Adverse Event Listing. Serious Treatment Emergent Adverse Events Related To Treatment
Enter your search here
DATACOLLECTION
ANALYSISSHOPPING CART
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SAMPLE TEMPLATE
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SDTMDOMAINSDTM VariableValue Level MetadataControlled TerminologyComputational algorithm
ADaMDOMAINADaM VariableADaM ParametersControlled TerminologyComputational algorithm
CDASHDOMAINCDASH VariableValue Level MetadataControlled Terminology
Domain VariablesADaM Computational Algorithm
ADAE One record per subject per adverse event, per date
Dataset Description
x
Domain Variables Computational AlgorithmSDTMRelated metadata: Domain VariablesCDASH
Filter active: ADAE Domain
DCM Figures ListingsTables
DATACOLLECTION
ANALYSISSHOPPING CART
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SDTMDOMAINSDTM VariableValue Level MetadataControlled TerminologyComputational algorithm
ADaMDOMAINADaM VariableADaM ParametersControlled TerminologyComputational algorithm
CDASHDOMAINCDASH VariableValue Level MetadataControlled Terminology
ADaM Computational Algorithm
Domain Variables Computational AlgorithmSDTMRelated metadata: Domain VariablesCDASH
Filter active: ADAE Variables
Domain Variables
ADAE
Domain LabelName Computational Algorithm
ADAE
ADAE
ADAE
ADAE
ADAE
ADAE
ADAE
ADAE
USUBJID
SUBJID
SITEID
DOSEAONU
DOSEAEON
COUNTRY
ASTTM
ASTDT
AETERM
Unique Subject Identifier
subject identifier for the study
Study Site identifier
Study Drug Dose at AE Onset Units
Study Drug Dose at AE Onset
Country
Analysis Start Time
ADAE.DOSEAEONU
ADAE.DOSEAEON
ADAE.ASTTM
ADAE.ASTDT
Reported Term for the Adverse Events
Analysis Start Time
x
Filter active: ADAE Domain
DCM Figures ListingsTables
DATACOLLECTION
ANALYSISSHOPPING CART
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SDTMDOMAINSDTM VariableValue Level MetadataControlled TerminologyComputational algorithm
ADaMDOMAINADaM VariableADaM ParametersControlled TerminologyComputational algorithm
CDASHDOMAINCDASH VariableValue Level MetadataControlled Terminology
ADaM
Domain Variables Computational AlgorithmSDTMRelated metadata: Domain VariablesCDASH
Filter active: ADEA Computation..
Domain Variables Computational Algorithm
Reference Description
ADAE.AENDT Equals to % SDTM_DATE_VARIABLE % transformed into %DATE_NUMERIC_FORMAT% when length (%SDTM_DATE_VARIABLE%) > 9
x
ADAE.ADURN Equals to ADAE.AENDT – ADAE.ASTDT + 1.
ADAE.DOSEAEON Equals to EX.EXDOSE where the numeric version of EX.EXSTDTC <= ASTDT <= the Numeric version of EX.EXENDTC.
ADAE.DOSEAEONU Equals to EX.EXDOSU where the numeric version of EX.EXSTDTC<= ASTDT <= the Numeric version of EX.EXENDTC.
ADAE.DOSEAEON Equals to ‘’DAYS’’ADAE.DOSEAEON
Filter active: ADAE Variables
DCM Figures ListingsTables
DATACOLLECTION
ANALYSISSHOPPING CART
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SDTMDOMAINSDTM VariableValue Level MetadataControlled TerminologyComputational algorithm
ADaMDOMAINADaM VariableADaM ParametersControlled TerminologyComputational algorithm
CDASHDOMAINCDASH VariableValue Level MetadataControlled Terminology
ADaM
Domain Computational AlgorithmSDTMRelated metadata: DomainCDASHDCM Figures ListingsTables
Domain Variables Computational Algorithm
Reference Description
xADAE.DOSEAEON
Equals to EX.EXDOSE where the numeric version of EX.EXSTDTC <= ASTDT <= the Numeric version of EX.EXENDTC.
Filter active: DOSEAEON
AESTDTC
EXDOSE
EXTDTC
EXENDTC
Start date/Time of Adverse Event
Dose per administration Derived
CRF
CRF Perm
Name Label Origin Role Core
Start date/Time of treatment
End date/Time of treatment
CRF
Record Qualifier
Timing
Timing
Timing
Exp
Exp
Exp
x
Variables
AE
AE
EX
EX
AESTDAT
AESTIM Start Time
Dose
Units
Domain Name Question
EXAMONT
EXAMONTU
Start Date
x
EX
EX
EX
EXENDAT
EXENTIM
EXSTDAT
End Date
End Time
Start Date
Variables
DATACOLLECTION
ANALYSISSHOPPING CART
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DATACOLLECTION
SDTMDOMAINSDTM VariableValue Level MetadataControlled TerminologyComputational algorithm
ADaMDOMAINADaM VariableADaM ParametersControlled TerminologyComputational algorithm
CDASHDOMAINCDASH VariableValue Level MetadataControlled Terminology
ANALYSISDomain VariablesADaM Computational Algorithm
Domain Variables Computational AlgorithmSDTMRelated metadata: Domain VariablesCDASHFigures ListingsTables
DCM’s
x
SHOPPING CART
DCM
<
<<< >>
ListingsTablesDCM
Domain Variables Computational Algorithm Domain Variables
Figures
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Agenda
Introduction
Specify by Browsing
Metadata-driven Process
Call to Action
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Specification, Execution
SDTMDatasets
ADaMDatasets
ADaMGenerator
TFLGenerator
TFLs
ADaM Standard Macros TFL Standard Macros
ADaM Metadata Analysis Results Metadata
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Metadata-driven Process
User InteractionMetadataData
Study-Specific Input
Study Input Selector
Metadata-driven Script Generator
Selected TFLsLibrary Metadata
SDTMDatasets
ADaMDatasets
ADaMGenerator
TFLGenerator
TFLs
ADaM Standard Macros TFL Standard Macros
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What is a Concept Map
Concept maps = diagram which include “bubbles” representing concepts/ideas/things and labeled arrows that represent the relationships between the concepts/ideas/things.
Advantage = easier to draw and more accessible than more formal modeling diagrams (e.g. UML diagrams)
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Concept Map Legend
Type of statistical analysis required for an endpoint
Statistical Concept Computational Algorithm
Algorithm used to calculate some analysis variable or
parameter
SAS ScriptSAS script that implements
a standard, re-usable, computational algorithm
TFL Definition
Type of TFL used to represent some data or analysis results
Document produced within the Clinical Trial
Document
Healthcare Encounter
Administration to or visit with A healthcare facility or
healthcare provider.
Observation Result
A finding obtained by an observation.
A Specification for creating normalized datasets compliant
with CDISC standards.
CDISC DomainVariable
The name and/or label of an attribute when represented
as a column in normalized dataset
A defined point in time.
Time Point
Initiative Procedure
An invasive treatment or Diagnostic procedure.
An activity that administers A product to a subject
Substance Administration
A sample taken from aSubject or existing
Specimen for analysis.
SpecimenProduct
Anything made. Includes drugs and devices.
Terminology
Verbatim terms or vocabularyfrom a codelist or dictionary.
Identifiers and relative timingused to describe a concept
as it relates to the study.
Study Context
An activity with multiple classifications. Often has component sub-activities.
Composite Activity
An activity that takes a Sample for later analysis
Specimen Collection
Assessor
The person or organization that reports ab observation.
Observation
A test, examination, or other activity that gathers
Information about a subject.
Other Activity
An activity not otherwise classified.
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Tabulation concept map
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Analysis Concept Map
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depicted by
presented in
for parameter
calculated using
implemented by
defined as Defined as
meaning that
defined as defined as defined as defined as
meaning that meaning thatcalculated using
equalstaking as input implemented by
using
takes as input
equalsequals
equalsmeaning that
as
asdefined in
specified in
Primary Efficacy Endpoint
TFL
CSR Section
Statistical Analysis Plan
Protocol Mean change from baseline
Table comparing drug and placebo arms on visit 1 seperated by age groups
Temperature PARAMCD
MT.ADVS.CHG
AVAL
BASE
mt_advs_chg,sas
Analysis set Selection Criteria Statistical Method Analysis Sub -Groups Data Handling Rule
Last Observation Carried Forward
DTYPE
LOCFmt_adsl_agegr.sasADSL.AGE
MT.ADSL.AGEGR1
18 to 65; over 65ANOVAVisit 1Efficacy Population
ADSL.EFFFL AVISIT
Visit 1Y
TEMP corresponds to VSSTRESN where VSTESTCD=TEMP
has
has
STUDYID
VISIT
VISITNUM
USUBJID
VSTESTCD
VSORRESU
VSDTC
VSTPT
VSTPTNUM
VSORRES
SUPPVS
VSSTRESC
C25206
C25206 TEMP
temperature
number
C
C42559
Study
number
visit
Temperature test
Temperature measurement
clinical significance
timepoint
stored in has
has
stored in equal to
stored in
stored in
stored in
stored in
stored in
results in
participates in
corresponds to
results in
reports
Subject
Assessor
clinical significance result
result unit
date
code
name
ID
assesses
stored in
equal to
equal to corresponds to stored in
corresponds to stored in
VSTEST
VSSTRESN
stored in
stored in
taken from
has
Can be proven by
Hypothesis
The Tabulation and AnalysisConcept Map will link perfectly
TabulationConcept Map
Analysis Concept Map
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Simplified view
Computational Algorithm
Data Collection
Analysis
SDTMADaM
SDTM VLM Value
SDTM VLM Names
SDTM Variables
CDASH Variables
(e) CRFCode values
Analysis Variables
Analysis Datasets
ADaM Parameter Value
ADaM Parameter Names
Analysis Results
TFLs
Endpoints
Code List
Domain
Only Semantic Interoperability can achieve real benefits from clinical data standards.
Colo
r Co
ding
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Tabulation Implementation Layer
has
has
STUDYID
VISIT
VISITNUM
USUBJID
VSTEST
VSTESTCD
VSORRESU
VSDTC
VSTPT
VSTPTNUM
VSORRES
SUPPVS
VSSTRESNVSSTRESC
C25206
C25206 TEMP
temperature
number
C
C42559
Study
number
visit
Temperature test
Temperature measurement
clinical significance
timepoint
stored in
stored in
stored in has
has
stored in equal to
stored in
stored in
stored in
stored in
stored in
results in
participates in
corresponds to
results in
reports
Subject
Assessor
clinical significance result
result unit
date
code
name
ID
assesses
stored in
equal to
equal to corresponds to
stored in
stored in
corresponds to
SDTM Variables
Protocol
Controlled Terminology
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Analysis Implementation Layer
has
can be proven by
depicted by a
presented in
for parameter
calculated using
implemented by
defined as
meaning that
defined as defined as defined as defined as
meaning that meaning thatcalculated using
equalstaking as input implemented by
using
taken as input
equalsequals
equalsmeaning that
as
asdefined in
specified in
Study
hypothesis
Primary Efficacy Endpoint
TFL
CSR Section
Statistical Analysis Plan
Protocol Mean change from baseline
Table comparing drug and placebo arms on visit 1 seperated by age groups
Temperature PARAMCD TEMP
MT.ADVS.CHG
AVAL
BASE
Mt_advs_chg.sas
Analysis set Selection Criteria Statistical Method Analysis Sub -Groups Data Handling Rule
Last Observation Carried Forward
DTYPE
LOCFMt_adsl_agegr.sasADSL.AGE
MT.ADSL.AGEGR1
18 to 65; over 65ANOVAVisit 1Efficacy Population
ADSL.EFFFL AVISIT
Visit 1Y
SAS Code
Protocol
Controlled Terminology
ADaM Variables
SAP/CSR
Computational Algorithm
Statistical Analysis Plan
Mean change from baseline
Table comparing drug and placebo arms on visit 1 seperated by age groups
Temperature PARAMCD
MT.ADVS.CHG
Mt_adsl_agegr.sas
ADSL.EFFFL AVISIT
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Display Table 10-1.01 Primary Endpoint Analysis: ADVS-Temperature - Summary at Visit 1 - LOCF (Efficacy Population)
Analysis Result Dose Response Analysis for ADVS.TEMP changes from baseline
Analysis Parameter(s) PARAMCD = "TEMP"
Analysis Variable(s) CHG (Change from Baseline)
Analysis Reason SPECIFIED IN SAP
Analysis Purpose PRIMARY OUTCOME MEASURE
Data References (incl. Selection Criteria) ADVS [PARAMCD = "TEMP" and AVISIT = "Visit 1" and EFFFL = "Y"]
DocumentationLinear model analysis of CHG for dose response using randomized dose (0mg for placebo; 10mg for low dose; 50mg for high dose) and site group in model. Used PROC GLM in SAS to produce p-value (from Type III SS for treatment dose). SAP Section 10.1.2
Programming Statements
[SAS version 9.2]
proc glm data = ADVS; where EFFFL='Y' and AVISIT='Visit 1' and PARAMCD="TEMP"; class AGEGR1; model CHG = TRTPN AGEGR1;run;
Implementation example:Analysis Results Metadata
Table comparing drug and placebo arms on visit 1 seperated by age groups
Temperature Mean change from baseline
PARAMCD
MT.ADVS.CHG
Statistical Analysis Plan
Mt_adsl_agegr.sas
ADSL.EFFFL AVISIT
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Agenda
Introduction
Specify by Browsing
Metadata-driven Process
Call to Action
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Gap Analysis 1
SDTM
VersionSDTM DomainSDTM VariableCode ListValue Level MetadataComputational AlgorithmData Handling Rules
ADaM
VersionADaM DomainADaM VariableCode ListComputational AlgorithmAnalysis Sub GroupsSAS Code
CDASHStandard
CRF Data Validation RuleData Collection ModuleCRF Template
VersionCDASH DatasetCDASH VariableCompletion GuidelinesImplementation Guidelines
Protocol& SAP
Clinical Study ProtocolStatistical Analysis PlanStatistical Requirements
Statistical Method
Analysis Results
Display Display LayoutFootnoteTitlesAnalysis RuleOutput Specification RuleSAS Code
Clinical Study Report
Clinical Study ReportCSR Section
Industry StandardNo industry Standard
Color CodingNot everything is standardized (yet)
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Gap Analysis 2
SDTM Variables
SAS Code
Protocol
Controlled Terminology
ADaM Variables
SAP/CSR
Computational Algorithm
Primary Efficacy Endpoint
TFL
CSR Section
Statistical Analysis Plan
Protocol Mean change from baseline
Table comparing drug and placebo arms on visit 1 seperated by age groups
Temperature PARAMCD
MT.ADVS.CHG
AVAL
Mt_advs_chg,sas
Analysis set Selection Criteria Statistical Method Analysis Sub -Groups Data Handling Rule
Last Observation Carried Forward
DTYPE
LOCFmt_adsl_agegr.sasADSL.AGE
MT.ADSL.AGER1
18 to 65; over 65ANOVAVisit 1Efficacy Population
ADSL.ETTFL AVISIT
Visit 1Y
TEMP VSSTRESN where VSTESTCD=TEMP
BASE
STUDYID
VISIT
VISITNUM
USUBJID
VSTESTCD
VSORRESU
VSDTC
VSTPT
VSTPTNUM
VSORRES
SUPPVS
VSSTRESC
C25206
C25206 TEMP
temperature
number
C
C42559
Study
number
visitTemperature test
Temperature measurement
clinical significance
timepoint
Subject
Assessor
clinical significance result
result unit
date
code
name
ID
VSTEST
VSSTRESN
has
has
VSTESTCDTEMP
stored in has
has
Is stored in Is equal to
stored in
Is stored in
stored in
stored in
stored in
results in
participates in
corresponds to
results in
reportsassesses
stored in
equal to
equal to corresponds to stored in
corresponds to stored in
VSTEST
stored in
stored in
as
depicted by a
presented in
for parameter
calculated using
implemented by
defined as
meaning that
defined as defined as defined as defined as
meaning that meaning thatcalculated using
equalstaking as imput implemented by
taken as input
equalsequals
equalsmeaning that
as
defined in
specified in
Corresponds to
taken from
using
has
Can be proven by
Hypothesis
Current metadata is tabular
We need concept metadata
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depicted by
presented in
for parameter
calculated using
implemented by
defined as Defined as
meaning that
defined as defined as defined as defined as
meaning that meaning thatcalculated using
equalstaking as input implemented by
using
takes as input
equalsequals
equalsmeaning that
as
asdefined in
specified in
Primary Efficacy Endpoint
TFL
CSR Section
Statistical Analysis Plan
Protocol Mean change from baseline
Table comparing drug and placebo arms on visit 1 seperated by age groups
Temperature PARAMCD
MT.ADVS.CHG
AVAL
BASE
mt_advs_chg,sas
Analysis set Selection Criteria Statistical Method Analysis Sub -Groups Data Handling Rule
Last Observation Carried Forward
DTYPE
LOCFmt_adsl_agegr.sasADSL.AGE
MT.ADSL.AGEGR1
18 to 65; over 65ANOVAVisit 1Efficacy Population
ADSL.EFFFL AVISIT
Visit 1Y
TEMP corresponds to VSSTRESN where VSTESTCD=TEMP
has
has
STUDYID
VISIT
VISITNUM
USUBJID
VSTESTCD
VSORRESU
VSDTC
VSTPT
VSTPTNUM
VSORRES
SUPPVS
VSSTRESC
C25206
C25206 TEMP
temperature
number
C
C42559
Study
number
visit
Temperature test
Temperature measurement
clinical significance
timepoint
stored in has
has
stored in equal to
stored in
stored in
stored in
stored in
stored in
results in
participates in
corresponds to
results in
reports
Subject
Assessor
clinical significance result
result unit
date
code
name
ID
assesses
stored in
equal to
equal to corresponds to stored in
corresponds to stored in
VSTEST
VSSTRESN
stored in
stored in
taken from
has
Can be proven by
Hypothesis
BiomedicalConcept Map
AnalysisConcept Map
Analysis concepts aremissing (today)
Gap Analysis 3
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Semantic Technology
Conclusion
Clarity Structure Complete Terminology Machine readable Reusable
Impact Assessment
Automation End-to-End
Traceability
Business Outputs
Exports to Support Today’s Process
Is this something the industry wants?Effort is needed on an industry scale
Source: Dave Iberson-Hurst CDISC Standards and the Semantic Web Slides 12th October 2015 PhUSE Annual Conference, Vienna
Restricted © Business & Decision Life Sciences 2016 All rights reserved.
Thank you
Restricted © Business & Decision Life Sciences 2016 All rights reserved.
Peter Van Reusel | Managing Director | +32 476 54 59 17 | [email protected]