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

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Presentation on theme: "CDISC ADaM 2.1 Implementation: A Challenging Next Step in the Process Presented by Tineke Callant 2014-03-14."— Presentation transcript:

1 CDISC ADaM 2.1 Implementation: A Challenging Next Step in the Process Presented by Tineke Callant

2 2 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

3 3 Clinical Data Interchange Standards Consortium - Introduction Inception 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 ± 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

4 4 Clinical Data Interchange Standards Consortium - Introduction

5 5 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

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

7 7 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

8 8 CDISC - Foundational standards

9 9 content transport

10 10 CDISC - Foundational standards

11 11 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.

12 12 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

13 13 CDISC ADaM V2.1 - Analysis data flow ADaM

14 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

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

16 16 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

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

18 18 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

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

20 20 CDISC ADaM V2.1 - ADaM data structures Other CDISC ADaM Basic Data Structure for Time-to-Event Analysis Version May 8, 2012 CDISC ADaM Data Structure for Adverse Event Analysis Version May 10, 2012

21 21 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

22 22 CDISC ADaM V2.1 - Analysis data flow ADaM

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

24 24 CDISC ADaM V2.1 - Traceability Strategies for implementing SDTM and ADaM standards Susan 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

25 25 CDISC ADaM V2.1 - Traceability Traceability

26 26 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

27 27 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

28 28 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 CDISC ADaM V2.1 - Traceability Strategies for implementing SDTM and ADaM standards Susan Kenny – Michael Litzsinger

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

30 30 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 Strategies for implementing SDTM and ADaM standards Susan Kenny – Michael Litzsinger

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

32 32 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

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

34 34 CDISC ADaM V2.1 - ADaM metadata Microsoft Office Excel spreadsheet as framework analysis dataset %CHKSTRUCT(ds_ = )  Automatization  Compliance define.xml

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

36 36 CDISC ADaM V2.1 - ADaM metadata Analysis dataset metadata Illustration from CDISC ADaM V2.1 Practical consideration: ADxxxxxx ! ≠ SDTM ! The key variables should define uniqueness

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

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

39 39 Illustration from CDISC ADaM V2.1 CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata

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

41 41 Illustration from CDISC ADaM V2.1 CDISC ADaM V2.1 - ADaM metadata Analysis parameter value-level metadata

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

43 43 CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata in practice Analysis dataset metadata Analysis variable metadata Dataset nameDisplay format Variable nameCodelist / Controlled terms Variable labelSource / Derivation Variable type Parameter identifier (Basic Data Structure (BDS)) Analysis results metadata (not required)

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

45 45 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

46 46 Example CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata in practice...

47 47 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)

48 48 CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata in practice - Subposition in observation %CHKSTRUCT(ds_ = ADSL) 1212 ORDER

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

50 50 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] POSITION IN OBSERVATION SUBPOSITION IN OBSERVATION VARIABLE NAME 1STUDYID 2USUBJID 3SITEID 41SITEGR* 42SITEGR*N

51 51 Example CDISC ADaM V2.1 - ADaM metadata Analysis variable metadata in practice Example...

52 52 CDISC SDTMCDISC 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 metadata Analysis variable metadata in practice - Core Nulls are allowed

53 53 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

54 54 CHKSTRUCT macro Microsoft Office Excel spreadsheet as framework analysis dataset %CHKSTRUCT(ds_ = )  Automatization  Compliance define.xml

55 55 CHKSTRUCT macro - Automatization %CHKSTRUCT(ds_ = ADSL) Before After ORDER THE ANALYSIS VARIABLES

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

57 57 CHKSTRUCT macro - Automatization %CHKSTRUCT(ds_ = ADSL) Key variables Key variables Before After SORT THE ANALYSIS DATASET

58 58 CHKSTRUCT macro – Compliance Analysis datasetAnalysis variable metadata

59 59 CHKSTRUCT macro – Compliance Analysis datasetAnalysis variable metadata

60 60 CHKSTRUCT macro – Compliance Analysis datasetAnalysis variable metadata

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

62 62 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

63 63 Linear method - Challenges and solutions Step 1

64 64 Linear method - Challenges and solutions Step 1 - CDISC SDTM Implementation Guide...

65 65 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

66 66 Linear method - Challenges and solutions Step 1 - CDISC SDTM Implementation Guide Challenge: Flexible variable length...

67 67 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 !

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

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

70 70 Example: LB.LBSCAT Linear method - Challenges and solutions Step 1 - CDISC SDTM Implementation Guide Challenge: Permissible variables Solution: [sdtm] ↔ %CHKSTRUCT(ds_ = )

71 71 Linear method - Challenges and solutions Step 2

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

73 73 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

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

75 75 Linear method - Challenges and solutions Step 3

76 76 Linear method - Challenges and solutions - Step 3 ADaM

77 77 Linear method - Challenges and solutions - Step 3 Challenge: 12 SDTM → 12 ADaM?!? SDTM ADaM ? ? ?? ? ? ? ? ? ? ? ?

78 78 Linear method - Challenges and solutions - Step 3 Solution: 1 central model + sponsor specific add-ons sponsor specific add-on central ADaM model domlist.sas7bdat varlist.sas7bdat codelist.sas7bdat domlist.sas7bdat varlist.sas7bdat codelist.sas7bdat domlist.sas7bdat varlist.sas7bdat codelist.sas7bdat 1 1 Convert Excel file to SAS datasets (by ADaM administrator) 2 2 Combine central model and sponsor specific add-on (by study programmer) 1

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

80 80 Linear method - Challenges and solutions Step 4

81 81 Linear method - Challenges and solutions - Step 4 Challenge: SDTM model no. 1, 2, 3... ? SDTM ADaM ? ? ?? ? ? ? ? ? ? ? ?

82 82 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 ...

83 83 Linear method - Challenges and solutions Step 5

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

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

86 86 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

87 87 Take home messages Message 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

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

89 89 Internet:


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