Showcasing CT Relationships for SDTM TS Dataset

Disclaimer

The views and opinions expressed in this blog entry are those of mine and do not reflect the official policy or position of CDISC.

In this blog, I want to highlight one part of a project deliverable from the Controlled Terminology (CT) Relationships subteam - metadata about CDISC CT for the SDTM TS dataset.

Background

Before going into detail, here is a bit about how this Standards Development team was established. The team began at the CDISC Working Group Meeting in 2017 at Silver Spring, Maryland, U.S.A. NCI EVS representatives raised maintenance issues that stemmed from drastically different publication cadence between CDISC CT and Implementation Guide. Volunteers also shared implementation challenges about CDISC CT. After much discussion, the attendees agreed to this general problem statement for a new development subteam to tackle:

Relationships between published terminology codelists and variable metadata are not explicit enough or are incomplete in published Implementation Guides (IG) or Therapeutic Area User Guides (TAUG).

Project Deliverables

Fast forward to today, the team recently finished reviewing all the SDTM v1.4 & SDTMIG v3.2 domain variables. A project deliverable is being compiled with two main components:

A model for expressing CT relationships for SDTM
Metadata that details the relationship between variables and CDISC CT codelists & terms, or external dictionaries

Problem Discussions

Of all the SDTM datasets reviewed, I find Trial Summary (TS) the most intriguing due to its complex CT requirements.

The SDTM TS dataset, by definition, is "a trial design domain that contains one record for each trial summary characteristic." [1] A trial summary characteristic is represented by two parts: 1) TSPARM/TSPARMCD pair, or parameter/parameter code, respectively; and, 2) TSVAL, or value. Permissible values for TSVAL are dependent on TSPARM/TSPARMCD. In other words, CT requirement for TSVAL is dependent on TSPARM/TSPARM for any given dataset record.

Here is an excerpt from the SDTMIG v3.2's Appendix C1:

#	TSPARMCD	TSPARM	TSVAL (Codelist Name or Format)
1	ADDON	Added on to Existing Treatments	No Yes Response
2	TDIGRP	Diagnosis Group	SNOMED CT
3	PCLAS	Pharmacological Class of Inv Therapy	NDF-RT
4	TRT	Investigational Therapy or Treatment	UNII

Let's inspect and discuss each of them.

For #1, although seasoned CDISC users would likely recognize "No Yes Response" as one of the CDISC CT codelists, this notation inadvertently puts naive users at disadvantage. Even to trained users, it does not mean all the terms within that CT codelist are permissible. From a process automation's perspective, it contains no information to a machine about its purpose. Therefore, it isn't ideal for either human-, or machine-readability.

About #2 and #3, SNOMED CT and NDF-RT are external dictionaries. NDF-RT has been renamed to MED-RT. Not all users recognize these external dictionaries, especially when usages could be specific to certain geographical regions. Also, users face this implementation challenge: which component of these external dictionaries do they use to populate TSVAL? Therefore, information published in this SDTMIG appendix is not contemporary and is not explicit.

UNII is a coded identifier for all registered ingredients used in products regulated by US FDA. For example, 362O9ITL9D is the UNII for acetaminophen. In #4, it is misleading to populate TSVAL with UNII. It is more appropriate to populate this coded value in TSVALCD (parameter value code). The decode, so to speak, would instead go to TSVAL. In this instance, TSVAL shall correspond to the preferred substance name, a component in the Global Substance Registration System, which is maintained by U.S. FDA.

Solutions

What extra information is needed to make example #1 more readable to both human and machines? Since it is about CDISC CT, common attributes, such as codelist names (short & long) and c-codes will immediately be helpful. An attribute for subsetting codelist will be necessary to specify permissible values.

About the external dictionaries in examples #2 through #4, extra information to describe them will be elucidating, such as 1) owning organization, 2) dictionary's name, and, 3) dictionary's component.

An extra bit of metadata will be essential to cope with multiple regulatory requirements for SDTM data submissions.

All of the above together formulates the model (or, structure) for complete disambiguation of the relationships between CDISC CT and SDTM variables. The following tables illustrate this model in a tabular manner, along with the example parameters:

For use when CDISC CT is relevant:

#

Usages

Domain

Variable

Condition 1

C-Code for Value in Condition 1

Condition 2

C-Code for Value in Condition 2

CDISC CT Codelist Short Name

CDISC CT Codelist C-Code

CDISC CT Codelist Long Name

Permissible Value from CDISC CT

Permissible Value's C-Code

Health Authority Provisions

Context of which this row of metadata applies; valid values are versioned foundational standards

A domain abbreviation found in foundational standard in "Usages"

A variable name

May be used for normalized datasets such as SuppQual, Findings domains, and TS

** Use this for TESTCD and PARMCD; or, QNAM

Conditional Value's c-code in the Condition column, if applicable

May be used for normalized datasets such as SuppQual, Findings domains, and TS

** Use this for TEST and PARM to pair with TESTCD and PARMCD; otherwise, not needed

Conditional Value's c-code in the Condition column, if applicable

The CDISC CT Codelist that controls the values referenced in "Domain" and "Variable" columns

C-code that pairs with "CDISC CT Codelist Short Name"

Long name that pairs with "CDISC CT Codelist Short Name"

A semi-colon delimited value list subset from the codelist referenced in "CDISC CT Codelist Short Name"

C-codes for each value in "Permissible Value from CDISC CT", also semi-colon delimited

Specify to which health authority this set of metadata is applicable. Leave blank when not applicable. Example: "US FDA", "Japan PMDA"

1

SDTMIG v3.2

TS

TSVAL

TSPARMCD EQ "ADDON"

C49703

TSPARM EQ "Added on to Existing Treatments"

C49703

NY

C66742

No Yes Response

N; Y

C49488; C49487

For use when external dictionary is relevant:

#	Usages	Domain	Variable	Condition 1	C-Code for Value in Condition 1	Condition 2	C-Code for Value in Condition 2	External Dictionary's Organization	External Dictionary's Name	External Dictionary's Component	Descriptive Information	Health Authority Provisions
	Context of which this row of metadata applies; valid values are versioned foundational standards	A domain abbreviation found in foundational standard in "Usages"	A variable name	May be used for normalized datasets such as SuppQual, Findings domains, and TS ** Use this for TESTCD and PARMCD; or, QNAM	Conditional Value's c-code in the Condition column, if applicable	May be used for normalized datasets such as SuppQual, Findings domains, and TS ** Use this for TEST and PARM to pair with TESTCD and PARMCD; otherwise, not needed	Conditional Value's c-code in the Condition column, if applicable	Used when "Variable" is controlled by an external dictionary. Example: "MSSO", "Regenstrief Institute"	Used when "Variable" is controlled by an external dictionary. Example: "MedDRA", "LOINC"	Used when "Variable" is controlled by an external dictionary. Example: "Preferred Term Code", "LOINC Code"	Additional information that is useful for implementers from a citable source ** Citable implementation information that can't be molded into detail metadata; or, regulatory agency's requirements	Specify to which health authority this set of metadata is applicable. Leave blank when not applicable. Example: "US FDA", "Japan PMDA"
2	SDTMIG v3.2	TS	TSVAL	TSPARMCD EQ "TDIGRP"	C49650	TSPARM EQ "Diagnosis Group"	C49650	International Health Terminology Standards Organisation (IHTSDO)	SNOMED CT	SNOMED CT Fully Specified Name	Appendix C of SDTMIG v3.2 specifies SNOMED CT. See FDA TCG section 6.6.1.1 Also see Notes in Appendix C of SDTMIG v3.2: If the study population is healthy subjects (i.e., healthy subjects flag is Y), this parameter is not expected.	US FDA
2	SDTMIG v3.2	TS	TSVALCD	TSPARMCD EQ "TDIGRP"	C49650	TSPARM EQ "Diagnosis Group"	C49650	International Health Terminology Standards Organisation (IHTSDO)	SNOMED CT	SNOMED CT Identifier (SCTID)		US FDA
3	SDTMIG v3.2	TS	TSVAL	TSPARMCD EQ "PCLAS"	C98768	TSPARM EQ "Pharmacologic Class"	C98768	Department of Veterans Affairs/Veterans Health Administration	Medication Reference Terminology (MED-RT)	Established pharmacologic class (EPC)	Note: Refer to citation in FDA TCG guidance. If the established pharmacologic class (EPC) is not available for an active moiety, then the sponsor should discuss the appropriate MOA, PE, and CS terms with the review division.	US FDA; Japan PMDA
3	SDTMIG v3.2	TS	TSVALCD	TSPARMCD EQ "PCLAS"	C98768	TSPARM EQ "Pharmacologic Class"	C98768	Department of Veterans Affairs/Veterans Health Administration	Medication Reference Terminology (MED-RT)	Alphanumeric unique identifier (NUI)		US FDA; Japan PMDA
4	SDTMIG v3.2	TS	TSVAL	TSPARMCD EQ "TRT"	C41161	TSPARM EQ "Investigational Therapy or Treatment"	C41161	U.S. Food and Drug Administration (US FDA)	Global Substance Registration System	Preferred substance name		US FDA; Japan PMDA
4	SDTMIG v3.2	TS	TSVALCD	TSPARMCD EQ "TRT"	C41161	TSPARM EQ "Investigational Therapy or Treatment"	C41161	U.S. Food and Drug Administration (US FDA)	Global Substance Registration System	Unique Ingredient Identifier (UNII)		US FDA; Japan PMDA

Project Status

The project deliverable is currently undergoing Internal Review per CDISC's standard development process. [2] All artifacts created by the team are available on the CDISC Wiki, along with a Read Me section. [3] The team expects Public Review to begin in 3rd quarter of 2020.

Expected Outcomes

The team operates with a tight alignment with CDISC's strategic goal to transform standards and clinical knowledge into a multidimensional representation to support automation. [4] Users can expect the metadata will be accessible via CDISC Library when it completes the development lifecycle. Future IG and TAUG may reference CT Relationships to keep concurrent with CDISC CT publication cadence. The team may incorporate additional kinds of CT relationships metadata, e.g., CT codetables. [5] Also an aspiration, the team, using the same methodology, will expand to cover CDASH, SEND, and ADaM.

Acknowledgements

I want to acknowledge these people for their contributions and domain expertise: Kristin Kelly (Pinnacle 21), Michael Lozano (Eli Lilly), Sharon Weller (Eli Lilly), Donna Sattler (BMS), Debbie O’Neill (Merck), Smitha Karra* (Gilead), Judith Goud (Nurocor), Swarupa Sudini (Pfizer), Anna Pron-Zwick (AstraZeneca), Craig Zwickl (Independent), Erin Muhlbradt* (NCI EVS), Fred Wood (TalentMine), Trish Gleason (BMS), Sharon Hartpence (BMS), Diane Wold (CDISC). Special thanks to Ann White for copyediting.

* denotes team co-lead, current and past