SDTM Validation Rules Sub-team CDISC INTRAchange Feb 26 th, 2014.
CDISC SDTM Basics
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Transcript of CDISC SDTM Basics
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CDISC Submission StandardsStudySAS Blog
CDISC – Clinical Data Interchange Standards Consortium
The mission of CDISC is to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare.
• SDTM – Study Data Tabulation Model • FDA – Food and Drug Administration• FDA desires SDTM data. Future: require SDTM
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July 2003, the FDA announced that the Study Data Tabulation Model (SDTM) developed by CDISC would be the standard format for sponsors of human drug clinical trials when submitting data to the FDA.
ODM (Operational Data Model), LAB (Laboratory Data Model),SDS (Submission Data Model), ADaM (Analysis Dataset Model) which are under development by CDISC are all XML based.
ODM- addresses data used during trials and analysesLAB - describes ECG and other laboratory data standards SDS - emphasizes data flow from database to regulatory submissionADaM- defines safety domains and efficacy variables to facilitate statistical analysis.
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WHY STANDARDS?65% - 75% submission information is associated with safety data. A big volume of listings (e.g. for CRT dataset and patient profile, etc.) is always included even in electronic submissions.
Only 30% of programming time is used to generate statistical results with SAS®, and the rest of programming time to familiarize data structure, check data accuracy, and tabulate/list raw data and statistical results into certain formats.
This non-statistical programming time because of CDISC’s uniform data structure, its useful functions.
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What is SDTM: Study Data Tabulation Model
SDTM
Animals Humans (SEND) (SDTMIG)
This model describes the contents and structure of data collected during a clinical trial
The purpose is to provide regulatory authority reviewers (FDA) a clear description of the structure, attributes and contents of each dataset and variables submitted as part of a product application
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Before SDTM• Domains = Yes• Standard Domain Names = No• Standard Variables = No• Standard Variable Names = NoResult• Reviewers had to familiarize themselves with unique domain names,variables and variable names used in an application - TIME CONSUMING• Pooling, joining datasets awkward, difficult• Good portion of review time spent “cleaning up the data”• Inefficient, error-prone
After SDTM• Domains = Yes• Standard Domain Names = Yes• Standard Variables = Yes• Standard Variable Names = YesResult•Standard Domain Names = Easy to Find Data• Standard Variables/Variable Names = Immediate Familiarity with the Data• Consistency• Minimal learning curve• TIME EFFICIENT
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Fundamentals of SDTM - DomainsDomain: Collection of observations with common topicCase Report Form ---- SDTM domain---- Dataset – A domain may collect data from more than one CRF form – Generally each domain is represented by a datasetEach domain has a unique two-character domain name (e.g., AE, CM, VS)Variables in domain begin with the domain prefix: (e.g., VSTESTCD)Domain structure: verticalTwo categories of domains: – CDISC Standard Domains (spelled out in detail in the Implementation Guide). – Custom Domains • Based on one of the General Observation Classes (findings, events, interventions) • Basic variables are outlined in the SDTM
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Interventions
Exposure
Conmeds
SubstUse
Findings
VitalsLabs
ECG
Incl/Excl SubjChar
Ques’aire
Micro MSv3.1.2
Micro MBv3.1.2
DrugAcctPhysExam
PK Paramv3.1.2
PK Concv3.1.2
Events
AE
Deviationsv3.1.2
Disposition
MedHx
ClinicalEventsv3.1.2
Demog
Special Purpose
Comments
SUPPQUAL
Trial Design (5 Tables)
RELREC
SubjElementsv3.1.2
SubjVisitsv3.1.2
SDTM = Study Data Tabulation ModelSDTM = Study Data Tabulation Model
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SDTM General Observation ClassesInterventions class captures investigational, therapeutic and
other treatments that are administered to the subject (with some actual or expected physiological effect) either as specified by the study protocol (e.g., “exposure”), coincident with the study assessment period (e.g., “concomitant medications”), or self-administered by the subject (such as alcohol, tobacco, or caffeine)
Events class captures planned protocol milestones such as randomization and study completion (“disposition”), and occurrences, conditions or incidents independent of planned study evaluations occurring during the trial (e.g., “adverse events”) or prior to the trial (e.g., “medical history”)
Findings class captures the observations resulting from planned evaluations to address specific tests or questions such as laboratory tests, histopathology, ECG testing, and questions listed on questionnaires. Most findings are measurements, tests, assessments, or examinations performed on a subject in the clinical trial
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SDTM Basics - Special-Purpose DatasetsNot Classified as Interventions, Events, or FindingsThey Have Special Rules – Demographics (DM) – Comments (CO): free text comments – Trial domains: to describe the design of a trial – RELREC dataset: represent the relationship
between datasets and records – SUPPQUAL: used for data items not included
in the SDTM standard
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Fundamentals of SDTM - Variables• CDISC categorizes variables as being– Required: variables need to be in the domains • Their values cannot be null– Expected: variables need to be in the domain • Some values may be null– Permissible: variables included in the domain as needed• CDISC categorizes variables into five roles – Identifier: identify the study, subject no., the sequence number – Topic: specify the focus of the observation (such as the name of the lab test) – Timing: describe the timing of the observation (Visit, Start/End date, Days, Time Points, Duration) – Qualifier: additional text or numeric values
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Fundamentals of SDTM - Variables• Variables Attributes– Variable Name: limited to 8-chars– Variable Label: <=40 chars– Variable Type: mainly characters– Variable Length: <=200 chars
• Date/time format – ISO 8601 is a text string YYYY-MM-DDT hh:mm:ss (not a SAS format) – Has ability to handle incomplete date – Example: December 15, 2003 13:14:17 ----- 2003-12-15T13:14:17 December 15, 2003 ------------------ 2003-12-15
• Controlled Terms or Format – Controlled terminology or text should be used instead of, or in addition to arbitrary number codes (No SAS format!!) – CT (**) published externally (ex: MedDRA or follow CDISC-specific terminology) – CT (*) value from a sponsor-defined code list
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Example
“Subject 9201 had mild fatigue starting on Day 3.”
Topic Variable = adverse event = fatigue Identifier Variable = subject ID = subject 9201 Timing Variable = start date = Day 3 Qualifier Variable = severity = mild
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Topic Identifier TimingGroupingQualifier
SynonymQualifier
RecordQualifier
Observation Record
ResultQualifier
VariableQualifier
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.
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).
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.
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.
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
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 all other SAE flag variables in the AE domain; and --BLFL, --POS and --LOC.
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SDTM Mapping examples 1:1 mapping
Date of birth on a CRF page → Column “BRTHDTC” in SDTM DM table (horizontal)
Sex on a CRF page → Column “SEX” in SDTM DM table (horizontal)
Weight and Height on a CRF page → Weight and Height in the column “VSORRES” of the SDTM VS table (vertical)
1:N mapping
Visit date on one CRF page → Visit date in many SDTM tables
M:1 mapping
Date of FU visit on a CRF page - Date of baseline Visit on another CRF page → Study day in SDTM
DIRECT: a CDM variable is copied directly to a domain variable without any changes other than assigning the CDISC standard label.
RENAME: only the variable name and label may change but the contents remain the same.
STANDARDIZE: mapping reported values to standard units or standard terminology
REFORMAT: the actual value being represented does not change, only the format in which is stored changes, such as converting a SAS date to an ISO8601 format character string.
COMBINING: directly combining two or more CDM variables to form a single SDTM variable.
SPLITTING: a CDM variable is divided into two or more SDTM variables.
DERIVATION: creating a domain variable based on a computation, algorithm, series of logic rules or decoding using one or more CDM variables.
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Implementation Challenges1. CDISC Variable NamesAll dataset column names must follow the
standards in the SDTM.Example:• Sponsor uses a variable named AEBGDT for thebeginning of an adverse event.• SDTM variable name is AESTDTC.• The sponsor must rename the variable.
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2. Fitting Data into the Findings Observation Class (1)
• Some datasets may be structured to facilitate analysis or theuse of code lists (e.g., one record per subject per visit), and will require transformation to conform to the structure of the Findings model (one record per measurement).
• Existing denormalized datasets could still be used andsubmitted as analysis datasets after columns are renamed
2. Fitting Data into the Findings Observation Class (2)
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XML
and
You
SDTM
ADaM
ODM
CDISC
Standards