A Brief Introduc/on to CDISC SDTM and Data Mapping

Transcription

1 A Brief Introduc/on to CDISC SDTM and Data Mapping

2 Agenda Flow of Clinical Trials Data The Problem Introducing CDISC Understanding SDTM Concepts of Data Mapping References 5/3/10 2

3 Flow of Clinical Trials Data Protocol CRF Design Actual Trial (Patient data collected in CRFs) CDM (Patient data in CRFs converted to Raw Datasets) Create Analysis Datasets Raw Datasets Create TLFs FDA Submission/Review 5/3/10 3

4 The Problem Protocol CRF Design LACK OF STANDARDS Actual Trial (Patient data collected in CRFs) CDM (Patient data in CRFs converted to Raw Datasets) Create Analysis Datasets Raw Datasets Create TLFs FDA Submission/Review 5/3/10 4

5 Introducing CDISC 5/3/10 5

6 Introduc/on to CDISC Non profit organiza7on in 2000 Clinical Data Interchange Standards Consor7um (CDISC) 1. Development of industry standards to support the a) electronic acquisi7on b) exchange c) submission and d) archiving of clinical trials data 2. Development of global, playorm independent standards 5. Improve data quality and accelerate product development 5/3/10 6

7 CDISC Standards Study Data Tabula/on Model (SDTM) Analysis Data Model (ADaM) Opera/onal Data Model (ODM) Laboratory Data Model (LAB) Case Report Tabula/on Data Defini/on Specifica/on (CRTDDS) (define.xml) Standard for Exchange of Nonclinical Data (SEND) Protocol Representa/on (PR) Clinical Data Acquisi/on Standards Harmoniza/on (CDASH) Trial Design Model (TDM) 5/3/10 7

8 Understanding SDTM Clinical Data Acquisition Standards Harmonization (CDASH) Study Data Tabulation Model (SDTM) Analysis Data Model (ADaM) Trial Design Model (TDM) Operational Data Model (ODM) Laboratory Data Model (LAB) Describes contents and structure of data collected during a clinical trial Purpose is to provide regulatory authority reviewers Protocol (FDA) Representation a clear description of the structure, attributes (PR) and contents of each dataset and variables submitted as part of a product application Case Report Tabulation Data Definition Specification (CRTDDS) (define.xml) Standard for Exchange of Nonclinical Data (SEND) 5/3/10 8

9 Understanding SDTM 1. Study Data Tabula7on Model V Study Data Tabula7on Model Implementa7on Guide: Human Clinical Trials V3.1.1 hbp:// 5/3/10 9

10 Before and APer SDTM Before APer Domains Domains Χ Standard Domain Names Standard Domain Names Χ Standard Variables Standard Variables Χ Standard Variable Names Standard Variable Names 5/3/10 10

11 Before and APer SDTM Before Reviewers had to familiarize themselves with unique domain names, variables and variable names used in an applica7on TIME CONSUMING Good por7on of review 7me spent cleaning up the data Inefficient, error prone APer Standard Domain Names = Easy to Find Data Standard Variables/Variable Names = Immediate Familiarity with the Data Consistency Minimal learning curve TIME EFFICIENT 5/3/10 11

12 CDISC Submission Metadata Model Seven dis7nct metadata abributes : 1. The Variable Name 2. A descrip7ve Variable Label, up to 40 characters 3. The data Type 4. The set of controlled terminology for the value or the presenta7on format of the variable (Controlled Terms or Format) 5/3/10 12

13 Before and APer SDTM 5. The Origin or source of each variable 6. The Role of the variable, which determines how the variable is used in the dataset. 7. Comments or other relevant informa7on about the variable or its data. 5/3/10 13

14 5/3/10 14 Types/Classes of Domains in SDTM Interven/ons Exposure Conmeds SubstUse Findings Vitals Labs ECG Incl/Excl SubjChar Ques aire Micro MS v3.1.2 Micro MB v3.1.2 DrugAcct PhysExam PK Param v3.1.2 PK Conc v3.1.2 Events AE Devia7ons v3.1.2 Disposi7on MedHx Clinical Events v3.1.2 Demog Special Domains Comments SUPPQUAL Trial Design (5 Tables) RELREC SubjElements v3.1.2 SubjVisits v3.1.2 The Interven'ons class, captures inves/ga/onal treatments, therapeu/c treatments, and surgical procedures that are inten/onally administered to the subject (usually for therapeu/c purposes) either as specified by the study protocol (e.g.,.exposure.), or preceding or coincident with the study assessment period (e.g.,.concomitant medica/ons.). The Events class, captures planned protocol milestones such as randomiza7on and study comple7on ( disposi7on ), and occurrences or incidents independent of planned study evalua7ons occurring during the trial (e.g., adverse events ) or prior to the trial (e.g., medical history ). The Findings class, captures the observa7ons resul7ng from planned evalua7ons to address specific ques7ons such as observa7ons made during a physical examina7on, laboratory tests, histopathology, ECG tes7ng, and ques7ons listed on ques7onnaires. In addi7on to the three general observa7on classes, a submission will generally include a set of other special purpose datasets of specific standardized structures to represent addi7onal important informa7on. General Observa/on Classes

15 Categoriza/on Of Variables in SDTM Variables Roles 1. Iden7fier 2. Topic 3. Timing 4. Rule 5. Qualifier Iden/fier Topic Timing Iden/fies the subject Iden/fies the focus Describes the start Qualifier Rule of the observa/on of the observa/on and end of the Describes the trait express an algorithm observa/on of the observa/on in the Trial Design model. Grouping Qualifiers Result Qualifiers Synonym Qualifiers Record Qualifiers Variable Qualifiers Core/Being 1. Required 2. Expected 3. Permissible 5/3/10 15

16 Categoriza/on Of Variables in SDTM Roles 1. Iden7fier 2. Topic 3. Timing STRESN. 4. Rule 5. Qualifier Variables Grouping Qualifiers Result Qualifiers are used to group Synonym Qualifiers Record Qualifiers describe the specific results together a collec/on specify an alterna/ve name for a define addi/onal azributes of the Variable Qualifiers associated with the topic variable of observa/ons par/cular variable in an observa/on. observa/on record as a whole are used to further modify or for a finding. within the same domain. E. g. MODIFY and DECOD, which (rather than describing a par/cular describe a specific variable within E. g. ORRES, STRESC, and E. g. CAT, SCAT, GRPID, SPEC are equivalent terms for a TRT or variable within a record). an observa/on and is only TERM topic variable E. g. REASND, AESLIFE, meaningful in the context of the BLFL, POS and LOC. variable they qualify. E. g. ORRESU, ORNHI, and ORNLO Grouping Qualifiers Result Qualifiers Synonym Qualifiers Record Qualifiers Variable Qualifiers Core/Being 1. Required 2. Expected 3. Permissible 5/3/10 16

17 Categoriza/on Of Variables in SDTM Variables Roles 1. Iden7fier 2. Topic 3. Timing 4. Rule 5. Qualifier Required Variables Expected Variables Variables need to be Permissible Variables Variables need to be in the domains Variables may be present in the domains Their values cannot be null in the domains Their values can be null can be included as needed Grouping Qualifiers Result Qualifiers Synonym Qualifiers Record Qualifiers Variable Qualifiers Core/Being 1. Required 2. Expected 3. Permissible 5/3/10 17

18 Categoriza/on Of Variables in SDTM An Example Observa7on Unique Subject Identifier Topic Subject 123 had a serious and severe headache starting on study day 2 Timing Qualifier 5/3/10 18

19 Categoriza/on Of Variables in SDTM Subject Identifier Dataset Structure Topic Qualifier Timing USUBJID AETERM AESER AESEV AESTDY Unique Subject Identifier Reported Term for the Adverse Event Serious Event Severity/ Intensity Study Day of Start of Event 123 HEADACHE YES SEVERE 2 5/3/10 19

20 Categoriza/on Of Variables in SDTM Dataset Structure Required Expected Permissible USUBJID AETERM AESER AESEV AESTDY Unique Subject Identifier Reported Term for the Adverse Event Serious Event Severity/ Intensity Study Day of Start of Event 123 HEADACHE YES SEVERE 2 5/3/10 20

21 Categoriza/on Of Variables in SDTM Example Events Data (MH) Identifiers Topic Timing Qualifiers 5/3/10 21

22 Characteris/cs of SDTM Domains Domains 1. Domain: Collec7on of observa7ons with common topic. Generally each domain is represented by a dataset. 2. Each domain has a unique two character domain name (e.g., AE, CM, VS) 3. Variables in domain begin with the domain prefix: (e.g., VSTESTCD) 5/3/10 22

23 Characteris/cs of SDTM Variables Variables 1. Variable Name: limited to 8 chars 2. Date/7me format ISO 8601 is a text string YYYY MM DDT hh:mm:ss (not a SAS format) 3. Controlled terminology 1. CT (**) published externally (ex: MedDRA or follow CDISC specific terminology) 2. CT (*) value from a sponsor defined code list 5/3/10 23

24 Concepts of Data Mapping Mapping Process Source Datasets: Standard A e.g.: Client s legacy standard datasets Target Datasets: Standard B e.g.: CDISC SDTM CDISC ADaM Client s new standards 5/3/10 24

25 Data Mapping Step 1 Mapping Domains Target Domains Source Domains DM DEMO AE AEV CM COM VAR.... CON MED 5/3/10 25

26 Data Mapping Step 2 Mapping Variables DM Target Domain Variables STUDYID DOMAIN USUBJID SUBJID RFSTDTC RFENDTC SITEID INVID INVNAM BRTHDTC AGE AGEU SEX COM DEMO VAR Source Domain Variables COMVAR.STUDYID COMVAR.USUBJID DM.SUBJID COMVAR._RFSTDT COMVAR._RFENDTC DM.SITEID DM.BRTHDTC DM.AGE DM.AGEU DM.SEX 5/3/10 26

27 Data Mapping Step 3 Mapping Variables Logic DM DEMO COM VAR STUDYID COMVAR.STUDYID Direct DOMAIN Mapping Logic Compute USUBJID COMVAR.USUBJID Direct SUBJID DM.SUBJID Direct RFSTDTC COMVAR._RFSTDT Date Conversion RFENDTC COMVAR._RFENDTC Direct SITEID DM.SITEID Direct INVID INVNAM BRTHDTC DM.BRTHDTC Direct AGE DM.AGE Direct AGEU DM.AGEU Direct Not mapping Not mapping SEX DM.SEX Value Conversion 5/3/10 27

28 Types of Mapping One to One Mapping Source CRF Variables SDTM Variables Date of birth BRTHDTC SEX SEX Weight VSORRES 5/3/10 28

29 Types of Mapping One to Many Mapping Source CRF Variables SDTM Variables VISIT DATE DTC DTC DTC 5/3/10 29

30 Types of Mapping Many to One Mapping Source CRF Variables SDTM Variables FU Visit Date STUDY DAY Baseline Visit Date 5/3/10 30

31 Automated Tool MapGenie 5/3/10 31

32 References 1. Prac7cal Methods for Crea7ng CDISC SDTM Domain Data Sets from Exis7ng Data Robert W. Graebner, Quin7les, Inc., Overland Park, KS 2. SDTM 3.1.1Implementa7onGuide.pdf 3. StudyDataTabula7onModelv1.1.pdf 5/3/10 32

33 5/3/10 33

34 Address Anindita Bhabacharjee 5/3/10 34