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Biometristi Italiani Associati – Seminario BIAS –CDISC SDTM and ADaM: Moving from theory to
practice SAS Institute – Milano 14/03/2014
Nicola Tambascia
Statistical Programming
Accovion GmbH
SDTM fundamentals
SDTM fundamentals 2
àStudy
àData
àTabulation
àModel
SDTM defines a standard structure for study data tabulations that are to be submitted as part of a product application to regular authority such as FDA. It based on material prepared by the Submissions Data Standard (SDS) Team of the CDISC.
SDTM IG Version
SDTM Version Accepted by FDA
3.1.1 1.13.1.2 1.23.1.3 1.33.2 1.4
What is SDTM?
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Application/Interfaces of CDISC Standards
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SDTM - Basic ConceptsDomains
Domain Classes
Domain Variables
Controlled Terminology (CT)
Metadata (define.xml)
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DomainsDomain = collection of observations with a common topic
• 1 Domain = 1 submission dataset (e.g., LB.XPT)
• Vertical structure
• 2 character domain name (e.g., LB)à 2 character variable name prefix (e.g. LBTEST)
• CDISC standard domains vs. custom domains
• Domain is assigned to 1 General Observation Classà Common variables per class (e.g., --TEST for Findings)
Laboratory Test
Standard Unit
Result in Standard Unit
Hemoglobin mg/dl 10
Hematocrit % 35
Hemoglobin (mg/dL)
Hematocrit (%)
10 35
NOT:
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Findings Variables Extract from SDTM 1.3
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Domain ClassesInterventions
Events
Findings, Findings About
Special Purpose/Other• Demographics• Comments• Subject Elements• Subject Visits
Trial Design
Relationships• Related Records to express relationships between domains• Supplemental Qualifiers
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Classes in SDTM V1.3§ 3 general observation classes and 1 special purpose class
provide the general structure for study data.
Classes
§ Planned in protocol milestones or independent occurrences taking place during the course of a trial
§ Prior to trial, e.g. medical history
§ Pre-planned data, e.g. randomization or study completion
§ Unplanned incidents, e.g. adverse events
§ Defined in protocol and applied during trial or preceding coincident to the study assessment
§ Investigational/ therapeutic treatments
§ Study/concomitant medications
§ Substances that are self administered by the subject, e.g. tobacco or alcohol
EVENTS INTERVENTIONS FINDINGS SPECIAL PURPOSE
§ Observations resulting from planned evaluations that are captured during the trial
§ Observations from planned/unplanned evaluations
§ Pharmacokinetics
§ Laboratory tests/ECG tests
§ Questionnaires
§ Additional subject level data that do not conform to one of the 3 general observation classes
§ Subject elements and visits
§ Comments
§ Demographics
For more information, please refer to the SDTM IG 3.1.3; sections 2.3, 2.4, 4, 5, 8.6.1
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Domains in SDTM IG V3.1.3
Events
Interventions
Findings
Classes Domains
Adverse Events (AE) Disposition (DS) Medical History
(MH)
Exposure (EX) Concomittant Medication (CM)
Substance Use(SU)
Laboratory Data (LB)
Vital Signs (VS)
Physical Examination (PE)
Inclusion/ Exclusion (IE)
SubjectCharacteristics
(SC)
ECG (EG)
Demogrphics(DM) Comments (CO) Subject Elements
(SE)
Questionnaires (QS)
PK Parameters (PP)
PK Concentrations
(PC)
Microbiology Specimen (MB)
Protocol Deviations (DV)
Clinical Events (CE)
Subject Visits (SV)
Trial Elements (TE) Trial Arms (TA) Trial Summary
(TS) Trial Visits (TV) Trial Inclusion/ Exclusion (TI)
Drug Accountability
(DA)
Microbiology Susceptibility
Test (MS)
Special Purpose
Trial Design
Relationship Related Records (RELREC)
SupplementalQualifiers(SUPP--)
Findings about Events and Interventions (FA)
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Class – InterventionsInvestigational, therapeutic andother treatments/substances administered
CDISC Standard Domains• Exposure (EX)
àStudy Drug Treatment
• Concomitant Medication (CM)
• Substance Use (SU) àE.g., Tobacco, Alcohol
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Sample EX Domain
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Class – EventsIncidents independent of planned study evaluations
Protocol milestones (e.g., randomization, completion)
CDISC standard domains
• Adverse Events (AE)
• Disposition (DS)à Protocol milestones (e.g., informed consent, study completion)
• Medical History (MH)
• Deviations (DV) à Major/minor Protocol Deviations
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Sample AE Domain
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Class – FindingsObservations resulting from planned evaluations
CDISC Standard Domains• ECG (EG)
• Incl/Excl Exceptions (IE)
• Labs (LB)
• Physical Exam (PE)
• PK Concentrations (PC)
• Questionnaires (QS)
• Subj. Characteristics (SC)
• Vital Signs (VS)
• Drug Accountability (DA)
• PK Parameters (PP)
Majority of data in findings structure• Test Code (- -TESTCD) , Name (- -TEST), Result (- -ORRES)
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Sample VS Domain
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Findings about Events or Interventions
Use Cases• Different timing than event/intervention
à Details collected in Findings" structure (occurrence at a fixed date/visit)
• Details on events/interventions which have qualifiers of their ownà E.g., units, methods
• Details on underlying disease when not collected as MHà FAOBJ=<underlying disease>
• Related symptoms or therapies• Occurrence of pre-specified AEs
à AEOCCUR=‘N‘ not allowed in AE domain
Combination of FAOBJ and FATESTCDà FAOBJ: Name of clinical event or conditionà FATESTCD: characteristic of event/condition
Class - Findings About
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Findings About (FA) - Sample
CE
FA
Express relationship in RELREC
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Class – Special PurposeDemographics (DM)
Comments (CO)• Separate comment page + comments on domain pages
Subject Elements
Subject Visits
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Demographics (DM)Structure 1 record per subject• General information on subject / treatment
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Comments (CO)Captures unstructured free-text comments
• Dedicated comments CRF page (discouraged by CDASH)
• Other CRF pages referring to a specific domain
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Class – Trial DesignPurpose
Concept
Trial Arms (TA)
Trial Elements (TE)
Trial Visits (TV)
Trial Inclusion/Exclusion (TI)
Trial Summary (TS)
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Trial Design Datasets - PurposeStandardized description of trial designs• Enables quick familiarization with trial design
• Facilitates comparison of different trials
• Enables search for trial design features in data warehouse
Linked with other SDTM domains
• Define arm and arm codes referenced in Demography (DM)
• Define visits used in findings datasets
• Define inclusion/exclusion criteria (only exceptions in IE)
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Trial Design Datasets – Purpose cont.Planned design vs. actual trial observations• Elements: TE vs. SE, Visits: TV vs. SV
• Inclusion: TI vs. IE
Trial design datasets contain planned design per protocol
Subject observation datasets contain observation as occurred in study • incl. unscheduled elements/visits
• Include observed observations onlyàPremature end
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Trial Design Data Sets - Concept
Elements: Building Blocks
(Screen, Drug A, Drug B,
Open Drug A, Rescue) Branches
Arms:
Epochs:
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Trial Arms (TA)
Open Drug X => Open Drug A
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Trial Elements (TE)
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Trial Visits (TV)Clinical Encounters Usually Defined in Protocol
May Start and End in Different Elements
VISITNUM, VISIT, VISITDY in TVà Controlled terminology for planned visits in findings datasets
Sample VS Domain in MSG sample submission package: tv.xpt
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Trial Inclusion/Exclusion (TI)Criteria as defined in protocol
Versioning of criteria due to protocol amendments
IE
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Trial Summary (TS)Basic information about the study in vertical format
Planned study characteristics as per protocolTe
rmin
olog
y
Further details on TSVAL (Null Flavor, Code
value, Dictionary and version used
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Trial Summary Codes
and even more in SDTM IG 3.1.3 Appendix C3
CT to be applied
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Subject Elements (SE)
Focus on handling of unplanned elements
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Subject Visits (SV)
Focus on handling of unplanned visits
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Class – RelationshipRelationships • Related Records to express relationships between domains
(RELREC)àE.g., concomitant medication applied due to specific AE
• Supplemental Qualifiers for parent domainsàE.g., Clinically Significant Flag for lab assessments
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Expressing RelationshipsRelationship of multiple records in the same domain
à Same --GRPID value
à E.g., mark CM records that are part of a combo treatment
Relationships between records in different domainsà Domain RELREC
à Relationships expressed via same RELID value
à Includes contributing parent records identified viaUSUBJID, IDVAR, IDVARVAL
à Potential IDVARs: --SEQ, --GRPID
General relationships between domainsà Only 2 records in RELREC
à USUBJID, IDVARVAL are blank, --GRPID describes merge key
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RELREC SamplesRelationships between single records in different domains
General relationships between domains
• Prerequisite: all concentrations used for derivation of parameters
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Sample SUPPQUAL (SUPPAE)
SUPP- - dataset may also be created for custom domainsSource: CDISC SDTM/ADaM Pilot (selected variables)
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Parent Domains & SUPPQUAL
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Custom Domains vs. SUPPQUALIssue• Placement of variables not covered by standard domains
Solution• New topic (set of variables)
àCreate Custom Domain- Determine observation class (Findings, Interventions, Events)- New domain name (convention: X-, Y-, Z-)- Adapt variable names and labels accordingly
• Additional qualifier to existing domain (single variables)àCreate Supplemental Qualifier Dataset
- Naming convention: SUPP- - ( -- = Domain)- Link to parent domain record(s) via IDVAR, IDVARVAL
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Creating a Custom Domain According to SDTM IG
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Domain Variables
Role Concept
Core Variable Concept
Technical Requirements
Values Controlled via Terminology
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AE Domain Class Table from SDTM IG
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Domain Variables – Role ConceptIdentifier – Key Variables
• Study, Unique Subject ID, Domain Name, Sequence Number
Topic – Focus of Observation/Dataset
• E.g., Test Code for Findings, Verbatim Term for Events
Timing – Time Relevant Information
• Visits, Date/Times, Relative Study Days
Qualifier – Attributes/further Description of Topic
• Subcategories: Grouping, Result, Synonym, Record, Variable
• E.g., Type of Lab Test, Original Result, Test Name,Status=NOT DONE, LL/UL of Normal Range
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Core Variable ConceptCore=Required• Variable must be included in domain
• Variable cannot be Null
Core=Expected• Variable must be included in domain
• Variable can be Null (preferably not always)
Core=Permissible• Variable can be excluded/included in domain as needed
• Variables not populated should be omitted
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Variables – Technical RequirementsRestrictions due to SAS V5 transport format• Variable names + length of --TESTCD <= 8 characters
• Variable labels + length of --TEST <= 40 characters
• Character variables <= 200 characters
Attributes (data type, label, ...) + (order) must match• SDTM specifications
• Study specific metadata (define.xml)
Additional variables =>• Custom domain or supplemental qualifiers
• (SDTM domain if variable type is included in SDTM class)
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Controlled TerminologyControlled Terminology needed for• Description of variables with a discrete list of possible values
• Vertical structure calls for standard test names and codesàFacilitates analysis across submissions
Current situation• CDISC Terminology available for some but not all variables
àE.g., N/Y, Vital Signs Test Codes (PULSE, TEMP, ...)
• Sponsor specific terminology in addition
• CDISC terms preferred, transition period allowed
CDISC + sponsor terminology => describe in metadata
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http://www.cancer.gov/cancertopics/cancerlibrary/terminologyresources/cdisc
Controlled Terminology (Continued)
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Special Terminology: Date & DurationsISO 8601 Date/Time Format (-DTC Variables)
ISO 8601 Durations (-DUR Variables)
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Metadata – define.xmlMetadata = data about data
Human-readable description to familiarize with data• What is included?
• Where does the data come from?
• Derivation rules, if applicable
Machine-readable metadata to facilitate ...
• SDTM data consistency check
• Upload into data warehouse
Preferred => mandatory format: define.xml
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Metadata – define.xml
Excerpt from the CDISC Draft Metadata Submission Guidelines define.xml Sample
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Advantages of XML – Basic Technology<?xml version="1.0" encoding="ISO-8859-1"?><?xml-stylesheet type="text/xsl" href="define1-0-0.xsl"?><ODM...
<ItemGroupDef OID="DM"Name="DM"Repeating="No"IsReferenceData="No"Purpose="Tabulation"def:Label="Demographics"def:Structure="One record per subject"def:DomainKeys="STUDYID, USUBJID"def:Class="Special Purpose"def:ArchiveLocationID="Location.DM">...
style sheetreference
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define.xml Sections/ElementsData Metadata (TOC)
Variable Metadata
Variable Value Level Metadata
(Computational Algorithms)
Controlled Terminology/Code Lists
Annotated CRF
Optional: Supplemental Data Definition Document
Navigation via Bookmarks and Hyperlinks
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Data Metadata
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Variable Metadata
Origins in SDTM IG 3.1.3: CRF, Derived, Assigned, eDT, Protocol, Sponsor Defined
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Variable Value Level Metadata
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Controlled Terminology/Code Lists
External dictionary references incl. versions • e.g., MEDDRA, WHODRUG
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CRF AnnotationDecide where data collected have to be stored in SDTM
• Target destination is based on data contentà Data on 1 CRF page may map to multiple SDTM domainsà Target data structure to be considered in the mapping
• Use standard SDTM domains where possible• Custom domains for new topics
à Decide on appropriate observation class
• Additional qualifiers for parent domains, i.e. not included in standard set of variablesà Supplemental Qualifier Domain (SUPP--)
• Issues resulting from non-extensible CDISC Controlled Terminology
Aim: Illustrate the Mapping
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Annotated CRF (blankcrf.pdf)See CDISC Metadata Submission Guidelines (SDTM)
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Reading an OpenCDISC SDTM Validation Report
Structure and Layout
Case Study• Decide on
à Issues to be corrected
àAcceptable issues to be explained in study data reviewer’s guide
OpenCDISC validates for adherence to structural SDTM rules
Content validation still required
• E.g., Weight data mapped to Height not spotted by OpenCDISC
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OpenCDISC Validation Report - StructureDataset Summary
Issue Summary
Details
Rules
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Remaining Issues in Study Data Reviewer‘s Guide
http://www.phusewiki.org/wiki/images/2/2d/SDRG_v1.1_2013-05-13.zipPhUSE CSS Study data reviewers guide:
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What‘s new in SDTM IG 3.2New Presentation (PDF Portfolio of files)
New Domains• Interventions Class
à Exposure as Collected (EC)à Procedures (PR)
• Events classà Healthcare Utilization (HO)
• Findings Classà Death Details (DD)à Immunogenicity Specimen Assessment (IS)à Morphology (MO)à Subject Status (SS)à Microscopic Findingy (MI)à Reproductive System Findings (RP)
• Findings Aboutà Skin Response (SR)
• Trial Designà Trial Disease Assessments (TD)
Medical Device and Associated Persons IG
New added Variables and changes to existing domainsSDTM fundamentals
Accovion GmbHHelfmann-Park 10D-65760 Eschborn, GermanyTel. +49 6196 [email protected]
www.accovion.com
Senior StatisticalProgrammer
Nicola Tambascia
Questions?
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Why CDISC SDTM/ADaM?General Benefits of Standards
• Improve communication
• Time/resource savings
• Comparison across submissions
FDA‘s eStudy Data Draft Guidance (Feb 2014)
• Written official requirement that sponsors submit study data in a standardized electronic format
• Includes HOW to submit (standard format)
• Does NOT include WHAT to submità Driven by science, to be agreed with reviewing division
• Refers to study data standards resources web page
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Regulatory RequirementsSDTM + ADaM referenced in eCTD‘s Study Data Specifications V2.0
SDTM
ADaM
Details: FDA‘s web page: Electronic Common Technical Document (eCTD), Study Data Specifications
Results
Data
TraceabilityClinical Study Report
Analysis data + define (ADaM)Analysis results/data creation programs
legacy data (optional)
SDTM data + define + aCRF Option: raw data + define in legacy format
SEND (optional)
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SDTM Implementation Details Handling of free text
Handling of clinical significance data
Handling of “Check all that apply“
Splitting of domains
Unscheduled Visits
Controlled Terminology
Adding Variables to Standard Domains
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Handling of Free TextNon Result Qualifiers
Options for populating supplemental qualifiers• EXLOC=OTHER, QNAM=EXLOCOTH, QVAL=UPPER ABDOMEN
• EXLOC=ABDOMEN, QNAM=EXLOCOTH, QVAL=UPPER ABDOMEN
• EXLOC=UPPER ABDOMEN; No SUPPEX
Choice driven by analysis/sponsor requirements
• Categories/grouping of locations in analysis table
Upper Abdomen
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Handling of Free Text (continued)Result Qualifiers
Options for representation in SDTM• MS Standard:
SCTEST=EYE COLOR, SCORRES=OTHER, SCSTRESC=OTHER, EYECOTH IN SUPPSC=BLUEISH GRAY
• Other Possibility:àSCTEST=EYE COLOR, SCORRES=OTHER, SCSTRESC=BLUEISH GRAY
àSCTEST=EYE COLOR, SCORRES=OTHER, SCSTRESC=GRAY, EYECOTH IN SUPPSC=BLUEISH GRAY
Choice driven by analysis/sponsor requirements• Categories/grouping of results in analysis table
Blueish Gray
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Handling of Clinical Significance DataAssessment on record/test level• e.g., a certain lab test result assessed a clinically
significant by investigator
• Store flag in supplemental qualifier dataset
• Entry in SUPP-- with QNAM= --CLSIGàCan be linked to parent record
Assessment on multiple record level• e.g., overall assessment of ECG results at a certain
time point
• Separate record in parent domain
• --TEST = "Interpretation"
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Handling of "Check All that Apply"
Options• More specific naming and labeling
• Most relevant selection may be mapped to domain variable
• Driven by analysis/sponsor requirements
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Multiple Responses for FindingsMultiple records per --TEST possible
Sample EG:EGTESTCD=RHYRATE, EGTEST=Rhythm and Rate, EGORRES=ATRIAL FIBRILLATION
EGTESTCD=RHYRATE, EGTEST=Rhythm and Rate, EGORRES=ATRIAL FLUTTER
--SEQ or --SPID necessary to uniquely identify a record
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Splitting of DomainsPossible reason: size issue (CDER <= 400 MB, CBER <= 1 GB)
Split on --CAT level
Allow for retrospective combination• Same DOMAIN value and suffix
• Consistent variable lengths
• --SEQ unique over all split domains
• Separate SUPP---- datasets following the same concept
Sample:QS36, SUPPQS36 with DOMAIN and RDOMAIN = QS
Ask FDA
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Prespecified FlagNew qualifier variable --PRESP• Handling / marking of pre-specified term
• For use in interventions and events classes
• Rationale: higher incidences expected for pre-specified terms
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Unscheduled VisitsRepeated Measurements• e.g. to follow up on abnormal lab values
Multiple records for the same visit?
Repeated visit gets VISITNUM with Decimal?• VISITNUM=1, VISITNUM=1.01• Respective VISIT values?
à Missing value vs. “VISIT 1 – REPEAT 1“
Generic values VISIT=UNSCHEDULED, VISITNUM=9999for every unscheduled visit?
à Try to avoid already at data collection time
Consistency with TV and SV to be considered
Keys in define.xml should uniquely identify a record
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