SDTM Trial Summary Domain: Putting Together The TS Puzzle
PharmaSUG 2016 - Paper DS11SDTM Trial Summary Domain: Putting Together the TS PuzzleKristin Kelly, Accenture Accelerated R&D Services, Berwyn, PAJerry Salyers, Accenture Accelerated R&D Services, Berwyn, PAFred Wood, Accenture Accelerated R&D Services, Berwyn, PAABSTRACTThe SDTM Trial Summary (TS) domain was updated with new variables and implementation strategies with thepublication of the SDTM v1.3/SDTMIG v3.1.3. This was an attempt to make the domain more useful to reviewers andto be machine readable to facilitate data warehousing. The FDA has recently considered TS as being essential toinclude in a study submission. They have developed tools that check that the TS domain is populated according tothe guidance in the SDTMIG and the FDA’s Study Data Technical Conformance Guide (TCG). Despite the advice forimplementation, constructing an informative and complete TS domain can be challenging for sponsors in terms ofinterpreting the guidance and using the correct controlled terminologies. This paper will discuss some of thechallenges associated with populating specific TS parameters, and provide some solutions for addressing thesechallenges.INTRODUCTIONThe SDTM Trial Design domains represent the plan for what will be done to subjects, and what data will be collectedabout them in the course of a clinical trial, to address the trial's objectives. The Trial Design domains consist of thefollowing: Trial Arms (TA), Trial Disease Assessments (TD), Trial Elements (TE), Trial Inclusion/Exclusion (TI), TrialSummary (TS), and Trial Visits (TV).According to the SDTM Implementation Guide (SDTMIG) v3.2, the standard Trial Design Datasets will allowreviewers to: clearly and quickly grasp the design of a clinical trial compare the designs of different trials search a data warehouse for clinical trials with certain features compare planned and actual treatments and visits for subjects in a clinical trial.For these reasons, it is important that the trial-level domains reflect the study design as accurately as possible.Although each of the trial design domains provide a reviewer with important planned aspects of a clinical trial indataset format, the focus of this paper will be on the Trial Summary (TS) domain.The FDA has recently developed their own tools that run automated checks on the SDTM data when it is received aspart of a submission, and before it is passed to a reviewer. This is done in order to identify data conformance issuesearlier in an effort to save time downstream in the review process. The FDA has published business andconformance rules based on the SDTM standard and FDA data requirements. There is one domain, TS, that hasbecome increasingly useful to FDA because it provides a high-level overview of the study without having to go toodeep into the data or refer to the protocol. There are several rules published by FDA that pertain to the TS domainthat check the adherence to their expectations of the content of this dataset. With the publication of SDTMv1.3/SDTMIG v3.1.3, several new variables were added to the TS domain that increase its usefulness in terms ofmachine readability and data warehousing. Because of these reasons, it is important that the TS domain beconstructed properly. This paper will discuss the TS domain as a whole and also focus on some of the challengesencountered during development.TS BASICS – THE PUZZLE OUTLINEThe Trial Summary (TS) domain is a trial-level dataset that allows for the submission of a high-level view of a study ina structured format. Each record contains the value of a parameter or characteristic of the trial. It is used to recordbasic information about the study such as trial phase, protocol title, and trial objectives. It contains both planned andactual aspects of the trial, such as the number of subjects, and study start/end dates.The TS domain as a whole can be thought of as a jigsaw puzzle made up of interlocking pieces that need to be puttogether in just the right way to make a complete picture. Typically, when working on a jigsaw puzzle, the first thingone might do is pick out the pieces with the flat edges that would make up the borders, or outline, of the puzzle. In thecase of TS, these outline pieces could be compared to the variables that provide the structure for the dataset.Beginning in SDTMIG v3.1.3, several new variables were added to TS in an effort to make the submission of a1
SDTM Trial Summary Domain: Putting Together the TS Puzzle, continuedminimum set of parameters consistent across sponsors. These variables include TSVALNF, TSVALCD, TSVCDREF,and TSVCDVER. When specific parameter values cannot be provided, a new variable is used to submit reasons whyit is missing (TSVALNF). These new variables can also be populated with reference to terminology used to populateTSVAL and the version used (TSVCDREF and TSVCDVER). Because TS has become more standardized acrossstudies, this has facilitated machine readability and storage.Since TS is not a general-observation-class domain, there is a finite set of variables allowed, and no other variablescan be added to the domain. The variables are listed in Table 1 below and will be referenced throughout the sectionsof this paper. Please refer to the SDTM v1.3/SDTMIG v3.1.3 and later versions for more information.Variable NameVariable LabelTypeSTUDYIDStudy IdentifierCharDOMAINDomain AbbreviationCharControlledTerms,Codelist orFormatRoleCDISC NotesCoreReqIdentifierUnique identifier for a study.Two-character abbreviation forthe domain.Sequence number given toensure uniqueness within adataset. Allows inclusion ofmultiple records for the sameTSPARMCD, and can be usedto join related records.Identifier(DOMAIN)TSSEQSequence NumberNumIdentifierTSGRPIDGroup IDCharIdentifierTSPARMCDTrial SummaryParameter ShortNameCharTSPARMCDTopicTSPARMTrial ameter ValueCharTSVALNFParameter Null FlavorCharTSVALCDParameter ValueCodeChar2ReqPermReqReqResultQualifierValue of TSPARM. Example:“ASTHMA” when TSPARMvalue is “Trial Indication”.TSVAL can only be null whenTSVALNF is populated. Textover 200 characters can beadded to additional columnsTSVAL1-TSVALn.Null flavor for the value ofTSPARM, to be populated ifand only if TSVAL is null.PermResultQualifierThis is the code of the term inTSVAL. For example;6CW7F3G59X is the code forGabapentin, C49488 is thecode for Y. The length of thisvariable can be longer than 8to accommodate the length ofthe external terminology.ExpResultQualifierNULLFLAVORUsed to tie together a group ofrelated recordsTSPARMCD (the companion toTSPARM) is limited to 8characters and does not havespecial character restrictions.These values should be shortfor ease of use inprogramming, but it is notexpected that TSPARMCD willneed to serve as variablenames. Examples: AGEMIN,AGEMAXTerm for the Trial SummaryParameter. The value inTSPARM cannot be longerthan 40 characters. ExamplesPlanned Minimum Age ofSubjects, Planned MaximumAge of SubjectsReqExp
SDTM Trial Summary Domain: Putting Together the TS Puzzle, continuedVariable NameTSVCDREFTSVCDVERVariable LabelName of theReferenceTerminologyVersion of t CDISC NotesThe name of the ReferenceTerminology from whichTSVALCD is taken. Forexample; CDISC, SNOMED,ISO 8601.The version number of theReference Terminology, ifapplicable.CoreExpExpTable 1: Trial Summary Variable Structure from the SDTMIG1TRIAL SUMMARY PARAMETERS (TSPARMCD/TSPARM) – THE INTERIOR PUZZLEPIECESWithin the variable structure of the TS puzzle, are the ‘interior pieces’ or parameters (i.e., the data) that must be fittogether to provide a complete picture of a study. In order to make TS useful, a minimum number of trial summaryparameters should be provided as shown below in Table 2. Most of the parameters come from www.clinicaltrials.gov,and the controlled terminology shown below is aligned with that source. 1 Definitions of the parameters and controlledterminology values for TSPARMCD, TSPARM, and TSVAL (for CDISC defined codelists) are maintained by theNational Cancer Institute's Enterprise Vocabulary Services (NCI EVS)6. The TSPARMCD codelist is extensible,meaning more values can be added as needed to describe the trial.The column titled ‘Inclusion in TS’ in Table 2 indicates whether the parameter should be included in the dataset. If aparameter is listed as ‘Required’, the record should be included in the TS domain and either TSVAL or TSVALNFmust be populated. In cases where a parameter is ‘Conditionally Required’ or ‘If Applicable’, the presence andpopulation depend on the study design. For example, if subjects are planned to take a protocol-specified treatment incombination with the investigational drug (ADDON (Added on to Existing Treatments) ‘Y’), then a record forTSPARMCD ‘CURTRT’ should be included. If the condition does not apply for a particular study and TSVAL cannotbe populated, the corresponding TSPARMCD record need not be included. Please refer to the SDTMIG for specificconditions per parameter for those that are ‘Conditionally Required’. ‘Extensible Addition’ parameters are present inthe NCI TSPARMCD codelist and can be added as needed, depending on the protocol and any other information thesponsor wishes to add to the TS CRMDURCURTRTDCUTDESCTSPARMActualNumber ofSubjectsAdaptiveDesignAdded on toExistingTreatmentsPlannedMaximumAge ofSubjectsPlannedMinimumAge eMinimumDurationCurrentTherapy orTreatmentData CutoffDescriptionTSVCDREFInclusion inTSTSVAL (Codelist orFormat)NumberExtensibleAdditionExternal Link (If Applicable)RequiredNo Yes Response (C66742)http://www.cancer.gov/cdiscRequiredNo Yes Response (C66742)http://www.cancer.gov/cdiscISO 8601RequiredISO 8601ISO 8601RequiredISO 8601UNIIIf ApplicableSRS Preferred SubstanceName (or Device Name)ISO 8601If ApplicableISO 8601UNIIConditionallyRequiredSRS Preferred SubstanceName (or Device sp
SDTM Trial Summary Domain: Putting Together the TS Puzzle, continuedTSPARMCDDCUTDTCDOSEDOSFRQTSPARMData CutoffDateDose perAdministrationDosingFrequencyTSVCDREFISO 8601Dose y ofInvestigational ntionModelNARMSPlannedNumber ofArmsTrial eMeasurePharmacological Class ofInvest.TherapyPlannedNumber ofSubjectsTrial isRandomizedRandomization ierRoute cy iscUnit (C71620)ISO 3166-1 alpha-3Country Code (C66786)http://www.cancer.gov/cdiscRequiredNo Yes Response CTrial LengthISO 8601External Link (If iredCDISCINTTYPETSVAL (Codelist orFormat)ExtensibleAdditionCDISCDOSUInclusion inTSIf ApplicableSNOMED CTIntervention Model ion Type /cdiscRequiredISO 8601NumberRequiredRequiredTextIf ApplicableTextIf ApplicableTextRequiredTextIf CRequiredNo Yes Response SCISO calTrials.govor EUDRACExtensibleAdditionRoute of Administration(C66729)If ApplicableISO ltrialsregister.eu/http://www.cancer.gov/cdisc
SDTM Trial Summary Domain: Putting Together the TS Puzzle, STYPETBLINDTCNTRLTSPARMStudy EndDateSex ofParticipantsClinicalStudySponsorStudy StartDateStudy StopRulesStudy TypeTrial BlindingSchemaControl rial TitleTSVCDREFISO 8601CDISCTPHASETRTTTYPESTRATFCTTrial PhaseClassificationInvestigational Therapy orTreatmentTrial TypeStratificationFactorTSVAL (Codelist orFormat)RequiredISO 8601RequiredSex of Participants (C66732)DUNSRequiredData Universal NumberingSystem or D-U-N-S (DUNS)ISO 8601RequiredISO 8601RequiredCDISCCDISCCDISCSNOMEDTITLEInclusion inTSCDISCCDISCUNIICDISCExternal Link (If iredRequiredConditionallyRequiredStudy Type (C99077)Trial Blinding Schema(C66735)http://www.cancer.gov/cdiscControl Type MED CTConditionallyRequiredTrial Indication Type(C66736)RequiredTextTrial Phase uiredSRS Preferred SubstanceName (or Device Trial Type ionAny allowable variable name(e.g., AGE, SEX)Table 2: Required and Commonly Used TS ParametersPARAMETER NULL FLAVOR (TSVALNF)For ‘Required’ TS parameters where TSVAL cannot be populated, TSVALNF should be used. The controlledterminology associated with this variable is part of the ISO 21090 standard, ‘Health Informatics – Harmonized datatypes for information exchange’ which provides the idea of ‘null flavor’. A null flavor is data that provides additionalinformation when its primary piece of data is null (has a missing value), in this case, TSVAL. There are 14 terms inthe NULLFLAVOR codelist in the ISO 21090 standard. Please refer to the SDTMIG for the complete list. Thefollowing are some codes commonly used to populate TSVALNF in SDTM TS: PINF (Positive infinity) – Positive infinity of numbers. Typically used when there is no maximum agestipulated in the protocol.UNK (Unknown) – A proper value is applicable, but not known.NAV (Not available) – Information is not available at this time, but it is expected that it will be available later.Typically used for study end date if the study is ongoing or for pharmacological class if the study drugrequires a new class to be added to the applicable dictionary.NA (Not applicable) – No proper value is applicable in this context.PARAMETER VALUE (TSVAL) – CONTROLLED TERMINOLOGYFor TSVAL, there are several dictionaries or codelists associated with specific TS parameters that are noted for eachparameter in the ‘TSVCDREF’ column above in Table 2. Parameters for which there is no codelist mentioned areconsidered free text, e.g. TSPARMCD ‘TITLE’. Instances where TSVCDREF is ‘CDISC’ points to NCI EVSControlled Terminology6. The ‘TSVAL (Codelist or Format)’ column in Table 2 then provides the codelist name andcorresponding Concept Code, or C-Code, for that NCI codelist. The following are the terminology and formats used topopulate TSVAL:5
SDTM Trial Summary Domain: Putting Together the TS Puzzle, continued CDISC Controlled Terminology – maintained by NCI EVS. Values should come from the ‘CDISC SubmissionValue’ column in the NCI CT SDTM spreadsheet6 (e.g., TSPARMCD ‘ADDON’, ‘INTMODEL’, ‘TPHASE’)UNII (FDA Unique Ingredient Identifier) - used to standardize active ingredients in investigational drugs andcomparators (e.g., TSPARMCD ‘TRT’, ‘CURTRT’, COMPTRT’)SNOMED CT (The International Health Terminology Standards Organization’s (IHTSDO) SystematizedNomenclature of Medicine – Clinical Terms) - used to identify the medical condition or problem that theinvestigational product is intended to affect (e.g. TSPARMCD ‘INDIC’, ‘TDIGRP’)NDF-RT (The Veterans Administration’s National Drug File – Reference Terminology) - used to identify thepharmacologic class of all active investigational drugs (e.g., TSPARMCD ‘PCLAS’)Clinicaltrials.gov/Euradac – used to identify the study from the clinical trial registry (e.g., TSPARMCD ‘REGID’)DUNS (Dun and Bradstreet Data Universal Numbering System) – used to identify a sponsor organizationbased on the address provided in the protocol (e.g., TSPARMCD ‘SPONSOR’)ISO 8601 Standard – dictates the format for dates and durations (e.g., TSPARMCD ‘AGEMIN’, ‘LENGTH’,‘SSTDTC’)ISO 3166-1 alpha-3 Standard – provides a 3-letter country code for each country included in the study (e.g.,TSPARMCD FCNTRY’)Typically, the submission value (e.g., CDISC CT) or the preferred term (e.g., SNOMED) would be placed in TSVALfor the applicable parameter. The corresponding code for that value is placed in TSVALCD and the name of thereferenced terminology is populated in TSVCDREF. If there is a version associated with the terminology, as is thecase with CDISC CT, this would be noted in TSVCDVER. For records where TSVAL is left null, the applicable codefrom ISO 21090 (described above) is placed in TSVALNF and TSVCDREF is populated as ‘ISO 21090’.An example of a TS dataset is provided below in Table 3. Please note that not all Required variables and parametersare included, but only those necessary for demonstration purposes. This table may be referenced for certainparameters throughout other sections of this RT1GEM1CUTOFFDCUTDESC1CUTOFFDCUTDTC1GEMDOSE2DRUG ADOSE3DRUG ADOSE1DOSU1HLTSUBJI1INDICTSPARMAdded on toExistingTreatmentsPlannedMaximumAge ofSubjectsPlannedMinimumAge ofSubjectsCurrentTherapy orTreatmentData CutoffDescriptionData CutoffDateDose perAdministrationDose perAdministrationDose perAdministrationDose PINFP18YISO UNIIISO inoma of pancreasC49487CDISC2014-03-28700423003SNOMED6
SDTM Trial Summary Domain: Putting Together the TS Puzzle, 1STYPETSPARMInterventionModelInterventionTypeTrial LengthPlannedNumber ofArmsPharmacological Classof Invest.TherapyTrial isRandomizedStudy EndDateSex ofParticipantsClinicalStudySponsorStudy StartDateStudy Type11TBLIND1TCNTRL1TDIGRP1TINDTP1TPHASETRT1DRUG IndicationTypeTrial PhaseClassificationInvestigational 4-03-28P30MISO 86015Alkylating DrugN0000175558NDF-RTYC49488CDISCNAV2014-03-28ISO 21090BOTHC49636CDISCABC NALISO 8601C98388CDISC2014-03-28OPEN LABELC49659CDISC2014-03-28ACTIVEAdenocarcinoma of ENTC49656CDISC2014-03-28Phase II TrialC15601CDISC2014-03-28DRUG A9B87ACB9BUNII1TTYPETrial TypeEFFICACYC98791CDISC2014-03-282TTYPETrial TypeC49667CDISC2014-03-283TTYPETrial 284TTYPETrial TypeSAFETYC49663CDISC2014-03-28Table 3: Example TS DatasetA NOTE ON SEQUENCE NUMBER AND GROUP ID (TSSEQ AND TSGRPID)In the example dataset above in Table 3, it’s important to note the assignment of TSSEQ and use of TSGRPID. Perthe SDTMIG for TS, multiple records of the same parameter should have a unique value of TSSEQ assigned. If thereis only one record for a particular parameter (or TSPARMCD/TSPARM) a TSSEQ 1 is assigned. In the datasetexample, there is only one record each for TSPARMCD ‘TPHASE’ and ‘TRT’, thus TSSEQ 1 for each parameter.There are four records created for TSPARMCD ‘TTYPE’ and each is numbered sequentially as TSSEQ 1, 2, 3,and 4. This is different from --SEQ numbering in the
Table 1: Trial Summary Variable Structure from the SDTMIG1 TRIAL SUMMARY PARAMETERS (TSPARMCD/TSPARM) – THE INTERIOR PUZZLE PIECES Within the variable structure of the TS puzzle, are the ‘interior pieces’ or parameters (i.e., the data) that must be fit together to provide a complete picture of a study.
2. Assign the labels from the SDTM metadata master file (SDTM V1.4) 3. Cross check the dataset with SDTM master file to confirm a. if the variable type matches with SDTM master file b. if the variable name is correct c. if any required or expected variables in this SDTM domain are not included d. if any variables have unexpected control .
In this tutorial, we will demonstrate the use of the "trial design dataset editor" using the TA (Trial Arms) as an example. Starting the editor from within SDTM-ETL After having loaded an SDTM template or existing define.xml with SDTM-ETL mappings, create a study-specific instance of the desired domain, in this case the TA domain.
SDTM-ETL 4.0: Performing Unit Conversions in SDTM-ETL Author: Jozef Aerts, XML4Pharma Last update: 2021-04-20 Introduction For SDTM/SEND Findings domains, unit conversions are often necessary, especially for the population of -STRESN (numeric standardized result) v
In case LBLOINC was not provide it by the template, you can still add it using the menu "Insert - new SDTM Variable". . (e.g. by searching in the CDISC-CT Excel worksheet), but . explained in the tutorial "Performing Unit Conversions in SDTM-ETL". Generic mapping functions One can also use the function "loinc2sdtmlb" that is taking two .
PhUSE 2017 2 REVIEW OF SDTM CRT PACKAGE Figure 1 shows a beautiful flower to represent the complete SDTM CRT package, with each constituent part represented by a petal. These are the five constituents of the SDTM CRT package which includes Study Data Reviewers Guide (SDRG.pdf), Pinnacle21 report, Blank CRF (contains SDTM variable mappings.
SDTM-ETL 3.1 User Manual and Tutorial Author: Jozef Aerts, XML4Pharma Last update: 2014-07-14 CDISC Dataset-XML Generation Sofar we have generated SDTM datasets using the option „Generate Transformation (XSLT) Code for SAS-XPT) although we did not create SAS datasets yet, and only generated the SDTM tables with the software itself.
SDTM-ETL 3.1 User Manual and Tutorial Author: Jozef Aerts, XML4Pharma Last update: 2014-07-19 . 5 One only need to set the path to the favorite PDF viewer in the „properties.dat" file, as explained in the SDTM-ETL . is described in a special document „SDTM-ETL Scripting Language". In the current case the mapping script is very simply:
PharmaSUG 2013 - Paper DS03 Programming Validation Tips for SDTM prior to using OpenCDISC validator Dany Guerendo, STATProg LLC, Morrisville, NC . do not, you can create a dataset from the SDTM IG excel spreadsheet listing all domains, also available online by following the links to SDTM standards on the CDISC website: www. CDISC.org. This .
