ADaM 2.1 Implementation: A Challenging Next Step in the Process

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

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

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 2

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 3

Clinical Data Interchange Standards Consortium - Introduction 4

Clinical Data Interchange Standards Consortium - Introduction  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 5

Clinical Data Interchange Standards Consortium - Introduction - 2001: Biomedical Research Integrated Domain Group (BRIDG) Model 6

CDISC - Foundational standards 8

CDISC - Foundational standards content transport 9

CDISC - Foundational standards 10

CDISC - Foundational standards  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.

CDISC ADaM V2.1 - Analysis data flow ADaM 13

CDISC ADaM V2.1 - ADaM data structures  The Subject-Level Analysis Dataset (ADSL) structure  The Basic Data Structure (BDS)  Other 15

CDISC ADaM V2.1 - ADaM data structures The 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 16

CDISC ADaM V2.1 - ADaM data structures  The Subject-Level Analysis Dataset (ADSL) structure  The Basic Data Structure (BDS)  Other 17

CDISC ADaM V2.1 - ADaM data structures The 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 18

CDISC ADaM V2.1 - ADaM data structures  The Subject-Level Analysis Dataset (ADSL) structure  The Basic Data Structure (BDS)  Other 19

CDISC ADaM V2.1 - ADaM data structures Other  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 20

CDISC ADaM V2.1 - Analysis data flow ADaM 22

CDISC ADaM V2.1 - Traceability  Understanding the relationship of element vs. predecessor  Enabling transparancy  Analysis results → Analysis datasets → SDTM 23

CDISC ADaM V2.1 - Traceability Strategies for implementing SDTM and ADaM standards Susan Kenny – Michael Litzsinger  Parallel method DBMS Extract SDTM Domains 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 24

CDISC ADaM V2.1 - Traceability  Traceability 25

CDISC ADaM V2.1 - Traceability  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 26

CDISC ADaM V2.1 - Traceability General 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 27

CDISC ADaM V2.1 - Traceability Strategies for implementing SDTM and ADaM standards Susan Kenny – Michael Litzsinger  Linear method DBMS Extract → SDTM Domains → Analysis Datasets    Traceability CDISC SDTM/ADaM Pilot Project Recommended 29

CDISC ADaM V2.1 - Traceability Strategies for implementing SDTM and ADaM standards Susan Kenny – Michael Litzsinger  Hybrid method DBMS Extract → SDTM Draft Domains → Analysis Datasets → SDTM Final Domains    Traceability Amendment 1 SDTM V1.2 and SDTM IG V3.1.2

Future?!?

CDISC ADaM V2.1 - Traceability  Traceability → Recommended: Linear method  Flexible  Delivery of consistent analysis datasets  Easy to use (Excel file)  Easy to maintain (Excel file) 31

CDISC ADaM V2.1 - ADaM metadata  Microsoft Office Excel spreadsheet as framework  Metadata 33

CDISC ADaM V2.1 - ADaM metadata  Microsoft Office Excel spreadsheet as framework  analysis dataset  %CHKSTRUCT(ds_ = )   Automatization Compliance  define.xml

CDISC ADaM V2.1 - ADaM metadata  Analysis dataset metadata  Analysis variable metadata  Analysis parameter value-level metadata  Analysis results metadata 35

CDISC ADaM V2.1 - ADaM metadata Analysis dataset metadata ! ≠ SDTM !

The key variables should define uniqueness  Illustration from CDISC ADaM V2.1

 Practical consideration: ADxxxxxx 36

CDISC ADaM V2.1 - ADaM metadata Analysis dataset metadata Analysis dataset naming convention  ADxxxxxx  The subject-level analysis dataset is named ADSL  max. 8 characters 37

CDISC ADaM V2.1 - ADaM metadata  Analysis dataset metadata  Analysis variable metadata  Analysis parameter value-level metadata  Analysis results metadata 38

CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata  Illustration from CDISC ADaM V2.1

CDISC ADaM V2.1 - ADaM metadata  Analysis dataset metadata  Analysis variable metadata  Analysis parameter value-level metadata  Analysis results metadata 40

CDISC ADaM V2.1 - ADaM metadata Analysis parameter value-level metadata  Illustration from CDISC ADaM V2.1

CDISC ADaM V2.1 - ADaM metadata  Analysis dataset metadata  Analysis variable metadata  Analysis parameter value-level metadata  Analysis results metadata (not required) 42

CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata in practice   Analysis dataset metadata Analysis variable metadata Dataset name Variable name Variable label Variable type Display format Codelist / Controlled terms Source / Derivation Parameter identifier (Basic Data Structure (BDS))  Analysis results metadata (not required) 43

CDISC ADaM V2.1 - ADaM metadata  Microsoft Office Excel spreadsheet as framework  Metadata 44

CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata in practice   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 45

CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata in practice  Example ...

CDISC ADaM V2.1 - ADaM metadata Analysis 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) 47

ORDER 1 CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata in practice Subposition in observation 1 2 2 %CHKSTRUCT(ds_ = ADSL) 48

CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata in practice Subposition in observation ORDER 1 2 1 2 49

CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata in practice Subposition in observation  Example  ADSL – SITEGR* (Char) and SITEGR*N (Num) * = a single digit [1-9] 2 3 4 POSITION IN OBSERVATION 1 4 1 2 SUBPOSITION IN OBSERVATION VARIABLE NAME STUDYID USUBJID SITEID SITEGR* SITEGR*N 50

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

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

CDISC SDTM

Req - Required

CDISC ADaM

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

Exp - Expected The variable must be included in the dataset.

Cond - Conditionally required ... and may contain some null values.

Perm - Permissible ... in certain circumstances.

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

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

 Nulls are allowed 52

CHKSTRUCT macro  Microsoft Office Excel spreadsheet as framework  analysis dataset  %CHKSTRUCT(ds_ = )   Automatization Compliance  define.xml

Before CHKSTRUCT macro - Automatization 4 6 5 7 1 2 3 After ORDER THE ANALYSIS VARIABLES %CHKSTRUCT(ds_ = ADSL) 1 2 3 4 5 6 7 55

Before CHKSTRUCT macro - Automatization After LABEL THE ANALYSIS VARIABLES %CHKSTRUCT(ds_ = ADSL) 56

CHKSTRUCT macro - Automatization Key variables Before 2 1 6 9 8 10 3 4 7 5 SORT THE ANALYSIS DATASET Key variables %CHKSTRUCT(ds_ = ADSL) After 1 2 3 4 5 6 7 8 9 10 57

CHKSTRUCT macro – Compliance Analysis dataset Analysis variable metadata 58

CHKSTRUCT macro – Compliance Analysis dataset Analysis variable metadata 59

CHKSTRUCT macro – Compliance Analysis dataset Analysis variable metadata 60

CHKSTRUCT macro  Excel spreadsheet as framework  Purpose    Reference Automatization Compliance 61

Linear method - Challenges and solutions

Step 1

Linear method - Challenges and solutions Step 1 - CDISC SDTM Implementation Guide ...

...

Linear method - Challenges and solutions Step 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 65

Linear method - Challenges and solutions Step 1 - CDISC SDTM Implementation Guide Challenge: Flexible variable length ...

...

Linear method - Challenges and solutions Step 1 - CDISC SDTM Implementation Guide Challenge: Flexible variable length 

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 !

Linear method - Challenges and solutions Step 1 - CDISC SDTM Implementation Guide Challenge: Flexible variable length  Traceability  Flexible  Delivery of consistent analysis datasets  Easy to use  Easy to maintain 68

Linear method - Challenges and solutions Step 1 - CDISC SDTM Implementation Guide Solution: [sdtm] ↔ %CHKSTRUCT(ds_ = ) 69

Linear method - Challenges and solutions Step 1 - CDISC SDTM Implementation Guide Challenge: Permissible variables Example: LB.LBSCAT

Solution: [sdtm] ↔ %CHKSTRUCT(ds_ = ) 70

Linear method - Challenges and solutions

Step 2

Linear method - Challenges and solutions Step 2 - SUPP-    QNAM QLABEL QVAL e.g. SUPPDM SDTM dataset → variable name → variable label → variable type → variable length e.g. ADSL ADaM dataset 72

Linear method - Challenges and solutions Step 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 73

Linear method - Challenges and solutions Step 2 - SUPP- Solution: [supp] ↔ %CHKSTRUCT(ds_ = ) 74

Linear method - Challenges and solutions

Step 3

Linear method - Challenges and solutions - Step 3 ADaM 76

1 2 Linear method - Challenges and solutions - Step 3 Challenge: 12 SDTM → 12 ADaM?!?

3 6 SDTM

ADaM

7 8 4 12 5 11 10 9 77

Linear method - Challenges and solutions - Step 3 Solution: 1 central model + sponsor specific add-ons central ADaM model domlist.sas7bdat

varlist.sas7bdat

codelist.sas7bdat

domlist.sas7bdat

varlist.sas7bdat

codelist.sas7bdat

sponsor specific add-on domlist.sas7bdat

varlist.sas7bdat

codelist.sas7bdat

1 1

Convert Excel file to SAS datasets (by ADaM administrator)

Combine central model and sponsor specific add-on (by study programmer) 78

Linear method - Challenges and solutions - Step 3 Solution: 1 central model + sponsor specific add-ons  Traceability  Flexible  Delivery of consistent analysis datasets  Easy to use  Easy to maintain 79

Linear method - Challenges and solutions

Step 4

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

3 6 SDTM

ADaM

7 8 4 12 5 11 10 9 81

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 ...

Linear method - Challenges and solutions

Step 5

Linear method - Challenges and solutions – Step 5 Challenge: Future 84

Linear method - Challenges and solutions – Step 5 Challenge: Future 85

  Take home messages Message no. 1 SDTM and ADaM go hand in hand Thus, without a CDISC compliant SDTM database to start from, ADaM cannot exist ADaM SDTM  But do realize a strong analysis data model needs more than a CDISC compliant SDTM database alone 87

Take home messages Message no. 2  Linear method:   Recommended Challenging  Solution:   SDTM: Central metadata repository ADaM: Automatization, e.g. [sdtm], [supp] … Study medata differences are handled efficiently 88

E-mail: [email protected]

Internet: www.sgs.com/cro 89

ADaM 2.1 Implementation: A Challenging Next Step in the Process

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

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