CDISC ADaM 2.1 Implementation: A Challenging Next Step in the Process Presented by Tineke Callant 2014-03-14

CDISC ADaM 2.1 Implementation:A Challenging Next Step in the Process

Presented by Tineke Callant

2014-03-14

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Agenda

CDISC - Introduction

CDISC - Foundational standards

CDISC ADaM V2.1 - Analysis data flow

CDISC ADaM V2.1 - ADaM data structures

CDISC ADaM V2.1 - Traceability

CDISC ADaM V2.1 - ADaM metadata

CHKSTRUCT macro

Linear method - Challenges and solutions

Take home messages

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Clinical Data Interchange Standards Consortium - Introduction

1997 - Inception

2000 - 32 global companies

CDISC is a global, open, multidisciplinary, non-profit organization that has established standards to support the acquisition, exchange, submission and archive of clinical research data and metadata.

2014 - ± 200 organizations biotechnology and pharmaceutical development companies device and diagnostic companies CROs and technology providers government institutions, academic research centers and other non-profit

organizations

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Mission statement

The CDISC mission is to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare.

Data standards to improve clinical research

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- 2001: Biomedical Research Integrated Domain Group (BRIDG) Model

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Agenda







CHKSTRUCT macro


Take home messages

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content

transport

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Study Data Tabulation Model (SDTM)

The content standard for regulatory submission of case report form data tabulations from clinical research studies.

Datasets containing data collected during the study and organized by clinical domain.

Analysis Data Model (ADaM)

The content standard for regulatory submission of analysis datasets and associated files.

Datasets used for statistical analysis and reporting by the sponsor, submitted in addition to the SDTM domains.

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Agenda







CHKSTRUCT macro


Take home messages

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ADaM

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Agenda







CHKSTRUCT macro


Take home messages

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The Subject-Level Analysis Dataset (ADSL) structure

The Basic Data Structure (BDS)

Other

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CDISC ADaM V2.1 - ADaM data structuresThe Subject-Level Analysis Dataset (ADSL) structure

One record per subject

Variables (required + other) Study identifiers (e.g. DM.STUDYID) Subject demographics (e.g. DM.AGE) Population indicator(s) (e.g. RANDFL) Treatment variables (e.g. DM.ARM) Trial dates (e.g. RANDDT)

Required in a CDISC-based submission

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Other

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CDISC ADaM V2.1 - ADaM data structuresThe Basic Data Structure (BDS)

One or more records per subject, per analysis parameter, per analysis time point (conditionally required)

Variables e.g. PARAM and related variables e.g. AVAL and AVALC and related variables e.g. the subject identification e.g. DTYPE e.g. treatment variables, covariates

Supports the majority of statistical analyses

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Other

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CDISC ADaM V2.1 - ADaM data structuresOther

CDISC ADaM Basic Data Structure for Time-to-Event Analysis Version 1.0 - May 8, 2012

CDISC ADaM Data Structure for Adverse Event Analysis Version 1.0 - May 10, 2012

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Agenda







CHKSTRUCT macro


Take home messages

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ADaM

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Understanding the relationship of element vs. predecessor

Enabling transparancy

Analysis results → Analysis datasets → SDTM


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CDISC ADaM V2.1 - TraceabilityStrategies for implementing SDTM and ADaM standardsSusan Kenny – Michael Litzsinger

Parallel method

SDTM Domains DBMS Extract

Analysis Datasets

Retrospective method

DBMS Extract → Analysis Datasets → SDTM Domains

Linear method

DBMS Extract → SDTM Domains → Analysis Datasets

Hybrid method

DBMS Extract → SDTM Draft Domains → Analysis Datasets → SDTM Final Domains

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Traceability

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CDISC ADaM V2.1 Fundamental principles

– Provide traceability between the analysis data and its source data

Practical considerations– Maintain the values and attributes of SDTM variables

CDISC ADaM implementation guide (IG) V1.0 General variable naming conventions

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CDISC ADaM V2.1 - TraceabilityGeneral variable naming conventions

Any ADaM variable whose name is the

same as an SDTM variable must be a

copy of the SDTM variable, and its label,

meaning, and values must not be

modified

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Parallel method

SDTM Domains DBMS Extract

Analysis Datasets

Retrospective method

DBMS Extract → Analysis Datasets → SDTM Domains

Linear method


Hybrid method



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Linear method


Traceability CDISC SDTM/ADaM Pilot Project Recommended


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Hybrid method


Traceability Amendment 1 SDTM V1.2 and SDTM IG V3.1.2 Future?!?


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Traceability → Recommended: Linear method

Flexible

Delivery of consistent analysis datasets

Easy to use (Excel file)

Easy to maintain (Excel file)


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Agenda







CHKSTRUCT macro


Take home messages

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Microsoft Office Excel spreadsheet as framework

Metadata

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analysis dataset

%CHKSTRUCT(ds_ = ) Automatization Compliance

define.xml

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Analysis dataset metadata

Analysis variable metadata

Analysis parameter value-level metadata

Analysis results metadata

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CDISC ADaM V2.1 - ADaM metadataAnalysis dataset metadata

Illustration from CDISC ADaM V2.1

Practical consideration: ADxxxxxx

! ≠ SDTM !The key variables should define uniqueness

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Analysis dataset naming convention

ADxxxxxx

The subject-level analysis dataset is named ADSL

max. 8 characters

CDISC ADaM V2.1 - ADaM metadataAnalysis dataset metadata

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CDISC ADaM V2.1 - ADaM metadataAnalysis variable metadata

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CDISC ADaM V2.1 - ADaM metadataAnalysis parameter value-level metadata

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Analysis results metadata (not required)

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CDISC ADaM V2.1 - ADaM metadataAnalysis variable metadata in practice



Dataset name Display formatVariable name Codelist / Controlled termsVariable label Source / DerivationVariable type

Parameter identifier (Basic Data Structure (BDS))

Analysis results metadata (not required)

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Metadata

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SAS variable attributes

To work in a SAS environment– NAME

– TYPE

– LENGTH

– FORMAT

– INFORMAT

– LABEL

– POSITION IN OBSERVATION

– INDEX TYPE

Analysis variable metadata fields

– DATASET NAME

– VARIABLE NAME

– VARIABLE LABEL

– VARIABLE TYPE

– DISPLAY FORMAT

– CODELIST /

CONTROLLED TERMS

– SOURCE / DERIVATION

– BASIC DATA STRUCTURE:PARAMETER IDENTIFIER

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Example


...

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CDISC ADaM V2.1 - ADaM metadataAnalysis variable metadata in practice - Subposition in observation

Example ADSL – SITEGR* (Char) and SITEGR*N (Num)

* = a single digit [1-9]

SITEID

SITEID grouped together by city in the variable SITEGR1 (SITEGR1N)

SITEID grouped together by province in the variable SITEGR2 (SITEGR2N)

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%CHKSTRUCT(ds_ = ADSL)

1 21 2ORDER

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ORDER 1 2 1 2

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Example ADSL – SITEGR* (Char) and SITEGR*N (Num)

* = a single digit [1-9]

POSITION IN OBSERVATION

SUBPOSITION IN OBSERVATION

VARIABLE NAME

1 STUDYID

2 USUBJID

3 SITEID

4 1 SITEGR*

4 2 SITEGR*N

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Example


Example

...

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CDISC SDTM CDISC ADaM

Req - Required

The variable must be included in the dataset and cannot be null for any record.

Req - Required

The variable must be included in the dataset.

Exp - Expected

... and may contain some null values.

Cond - Conditionally required

... in certain circumstances.

Perm - Permissible

The variable should be used in a domain as appropriate when collected or derived.

Perm - Permissible

The variable may be included in the dataset, but is not required.

CDISC ADaM V2.1 - ADaM metadataAnalysis variable metadata in practice - Core

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Agenda







CHKSTRUCT macro


Take home messages

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CHKSTRUCT macro


analysis dataset

%CHKSTRUCT(ds_ = ) Automatization Compliance

define.xml

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CHKSTRUCT macro - Automatization


Before

After

4 6 5 7 1 2 3

1 2 3 4 5 6 7

ORDER THE ANALYSIS VARIABLES

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Before

After

LABEL THE ANALYSIS VARIABLES

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Key variables

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2 1 3 4

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6 9 810

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1 2 3 4

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7 8 910

Key variables

Before

After

SORT THE ANALYSIS DATASET

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CHKSTRUCT macro – Compliance

Analysis dataset Analysis variable metadata

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CHKSTRUCT macro

Excel spreadsheet as framework

Purpose Reference Automatization Compliance

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Agenda







CHKSTRUCT macro


Take home messages

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Step 1

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Linear method - Challenges and solutionsStep 1 - CDISC SDTM Implementation Guide

...

...

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Linear method - Challenges and solutionsStep 1 - CDISC SDTM Implementation Guide

Any ADaM variable whose name is the

same as an SDTM variable must be a

copy of the SDTM variable, and its label,

meaning, and values must not be

modified

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Linear method - Challenges and solutionsStep 1 - CDISC SDTM Implementation GuideChallenge: Flexible variable length

...

...

...

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CDISC SDTM IG Variables of the same name in split datasets should have the same

SAS Length attribute Version 5 SAS transport file format: max. 200 characters -- TESTCD and QNAM: max. 8 characters -- TEST and QLABEL: max. 40 characters

Example: DM.RACE: $41, $50, and $200

Amendment 1 to SDTM V1.2 and SDTM IG V3.1.2 Version 5 SAS transport file format: max. 200 characters

! only if necessary !

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Traceability

Flexible


Easy to use

Easy to maintain


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Linear method - Challenges and solutionsStep 1 - CDISC SDTM Implementation GuideSolution: [sdtm] ↔ %CHKSTRUCT(ds_ = )

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Example: LB.LBSCAT

Linear method - Challenges and solutionsStep 1 - CDISC SDTM Implementation GuideChallenge: Permissible variables

Solution: [sdtm] ↔ %CHKSTRUCT(ds_ = )

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Step 2

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Linear method - Challenges and solutionsStep 2 - SUPP--

QNAM → variable name

QLABEL → variable label

QVAL → variable type

→ variable length

e.g. SUPPDM SDTM dataset e.g. ADSL ADaM dataset

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Linear method - Challenges and solutionsStep 2 - SUPP--Challenge: Flexible code list

QLABEL is different for the same QNAM– Example

ELIGCONF Subject Still Eligible

ELIGCONF Still Fulfill Eligibility Criteria

QLABEL format– Example

RANDNO RANDOMIZATION NUMBER

RANDNO Randomization Number

QLABEL changes during the course of a study– Example

ELIGIBLE Suject Eligible For Dosing

ELIGIBLE Subject Eligible For Dosing

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Linear method - Challenges and solutionsStep 2 - SUPP--Solution: [supp] ↔ %CHKSTRUCT(ds_ = )

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Step 3

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Linear method - Challenges and solutions - Step 3

ADaM

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Linear method - Challenges and solutions - Step 3Challenge: 12 SDTM → 12 ADaM?!?

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3

2

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910

SDTM

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ADaM

?

?

??

??

??

??

??

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Linear method - Challenges and solutions - Step 3Solution: 1 central model + sponsor specific add-ons

sponsorspecificadd-on

centralADaMmodel

domlist.sas7bdat

varlist.sas7bdat

codelist.sas7bdat

domlist.sas7bdat

varlist.sas7bdat

codelist.sas7bdat

domlist.sas7bdat

varlist.sas7bdat

codelist.sas7bdat

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1 Convert Excel file to SAS datasets (by ADaM administrator)

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2 Combine central model and sponsor specific add-on (by study programmer)

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Traceability

Flexible


Easy to use

Easy to maintain

Linear method - Challenges and solutions - Step 3Solution: 1 central model + sponsor specific add-ons

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Step 4

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Linear method - Challenges and solutions - Step 4Challenge: SDTM model no. 1, 2, 3 ... ?

1

3

2

4

5

6

8

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910

SDTM

12

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ADaM

?

?

??

??

??

??

??

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Linear method - Challenges and solutions - Step 4 Solution: Central metadata repository

CDISC metadata SDTM version SDTM metadata ...

Study characteristics Therapeutic area Clinical phase Trial design characteristics ...

Project metadata Study timelines Key Performance Indicators ...

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Step 5

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Linear method - Challenges and solutions – Step 5Challenge: Future

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Linear method - Challenges and solutions – Step 5Challenge: Future

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Agenda







CHKSTRUCT macro


Take home messages

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Take home messagesMessage no. 1

ADaM SDTM

SDTM and ADaM go hand in hand

Thus, without a CDISC compliant SDTM database to start from, ADaM cannot exist

But do realize a strong analysis data model needs more than a CDISC compliant SDTM database alone

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Linear method: Recommended Challenging

Solution: SDTM: Central metadata repository ADaM: Automatization, e.g. [sdtm], [supp] …

Study medata differences are handled efficiently

Take home messagesMessage no. 2

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E-mail:[email protected]

Internet:www.sgs.com/cro

Documents

CDISC ADaM 2.1 Implementation: A Challenging Next Step in the Process Presented by Tineke Callant 2014-03-14