CDISC Team Updates Webinar 24 May 2012

Transcription

1 CDISC Team Updates Webinar 24 May 2012

2 Agenda ADaM Time to Event Analyses - Sun Sook Kim ADaM Adverse Event - Deb Bauer Tuberculosis Standard - Jane Diefenbach Upcoming CDISC Events 2

3 ADaM Basic Data Structure for Time-to-Event Analyses Presenter: Sun Sook Kim Sr. Director of Biostatistics at BioMarin Author(s): ADaM ADTTE Subteam 3

4 Overview Path to ADTTE Version 1.0 ADaM Dataset Structure BDS (Basic Data Structure) Non-BDS ADTTE (Analysis Dataset for Time-to-Event) Characteristics of Analysis Variables of Interest Dataset Structure Document Structure 4

5 Path to ADTTE Version 1.0 Posting Draft in Jan Received 140 Comments (Jan April) Changes: Record-level Traceability (ASEQ) SAS Code in Results Metadata Editorial Changes for Clarification Publishing Final in May

6 ADaM Dataset Structure BDS (Basic Data Structure) 1 or more records /subject /analysis parameter /analysis time point (as applicable) e.g., ADTTE Non-BDS e.g., ADSL and ADAE 6

7 ADaM Dataset Structure - BDS Subject USUBJID Analysis Parameter PARAM, PARAMCD, Analysis Time Point AVISIT AVISITN, Weight (kg) Baseline Week 4 Week 8... Systolic BP (mm HG)... Baseline Week 4 Week Analysis Value AVAL AVALC,

8 ADaM Dataset Structure BDS (Basic Data Structure) 1 or more records /subject /analysis parameter /analysis time point (as applicable) e.g., ADTTE Non-BDS e.g., ADSL and ADAE 8

9 ADTTE Characteristics of TTE Analysis Variables of Interest ADaM Specific ADTTE Specific Dataset Structure - BDS 4 Examples 9

10 ADTTE Characteristics of Analysis Interested in Analyzing Incidence Time to the Event of Interest Graphical Presentation Hypothesis Testing Non-parametric Semi-parametric 10

11 ADTTE Graphical Presentation Kaplan-Meier Plot Time to Death (days) Death Rate (%) Treatment A Treatment B Days Since Randomization Note: The style of the display of the results of an analysis will be determined by the sponsor. The above example is intended to illustrate content and not appearance. 11

12 ADTTE Analysis Results Time to Death Through Day 168 by Treatment Group Analysis Population: Intent-to-Treat Time to Death (days) Treatment A (n=xxx) Treatment B (n=xxx) P value Method Median Time xxx.x (95% CI) a (xxx.x, xxx.x) xxx.x (xxx.x, xxx.x) 0.xxx Log-rank Test Event Rate (%) at Day 168 (95% CI) xx.x (xx.x, xx.x) xx.x (xx.x, xx.x) 0.xxx Cox Regression Model b N (%) Censored xx (xx.x%) xx (xx.x%) Note: Time to death is calculated as : date of death date of randomization. For subjects who did not die on or prior to Week 24 (Day 168), they are censored at Day 168. a Based on the Kaplan-Meier estimates b The Cox regression model includes treatment group, age, and sex as covariates Note: The style of the display of the results of an analysis will be determined by the sponsor. The above example is intended to illustrate content and not appearance. 12

13 ADTTE Variables of Interest ADaM Specific Analysis Sequence Number (ASEQ) ADTTE Specific Origin (e.g., STARTDT) Censor (CNSR) Event or Censoring Description (EVNTDESC) Censor Date Description (CNSDTDSC) 13

14 Variable Name ASEQ STARTDT ADTTE Variables of Interest Variable Label Analysis Sequence Number Time to Event Origin Date for Subject Type Codelist/ Controlled Terms Core CDISC Notes Num Perm Sequence number given to ensure uniqueness of subject record within a dataset. As long as values are unique within a dataset, any valid number can be used for ASEQ. Num Perm The original date of risk for the time-to-event analysis. This is generally the time at which a subject is first at risk for the event of interest evaluation (as defined in the Protocol or SAP). CNSR Censor Num * Cond CNSR is a required variable for a time-to-event analysis dataset, though it is a conditionally required variable with respect to the ADaM BDS. For example, CNSR=0 for event and CNSR >0 for censored records. EVNTDESC CNSDTDSC Event or Censoring Description Censor Date Description * User defined code list Char * Perm Describe the event of interest or an event that warrants censoring. Char * Perm Describe the circumstance represented by the censoring date if different from the event date that warrants censoring 14

15 ADTTE Examples Single Endpoint Binary Values for Censoring (Ex. 1) More than 2 Values for Censoring (Ex. 2) Composite Endpoint Censoring Reason and Date Explanation Provided in EVNTDESC (Ex. 3) Censoring Reason Provided in EVNTDESC while Censoring Date Explanation in CNSDTDSC (Ex. 4) 15

16 ADTTE BDS, Single Endpoint (Ex. 1) Time to Death with Censor = 0 (for event) or 1 (for censoring) USUBJID ASEQ PARAM PARAMCD AVAL STARTDT ADT CNSR EVNTDESC Time to Death (days) Time to Death (days) Time to Death (days) Time to Death (days) Time to Death (days) Time to Death (days) DEATH DEATH DEATH COMPLETED THE STUDY DEATH LTFU DEATH COMPLETED THE STUDY DEATH DEATH DEATH ADVERSE EVENT 16

17 ADTTE BDS, Single Endpoint (Ex. 2) Time to Death with Censor = 0 (for event) or > 1 (for censoring) USUBJID ASEQ PARAM PARAMCD AVAL STARTDT ADT CNSR EVNTDESC Time to Death (days) Time to Death (days) Time to Death (days) Time to Death (days) Time to Death (days) Time to Death (days) DEATH DEATH DEATH COMPLETED THE STUDY DEATH LTFU DEATH COMPLETED THE STUDY DEATH DEATH DEATH ADVERSE EVENT 17

18 ADTTE Multiple Values for Censoring For censor=0 (for event) and censor=1 (for censoring): PROC LIFETEST DATA=ADTTE; TIME AVAL*CNSR(1); STRATA TRTPN; RUN; For censor=0 (for event) and censor > 1 (for censoring): PROC LIFETEST DATA=ADTTE; TIME AVAL*CNSR(1 2 3); STRATA TRTPN; RUN; Note: The above codes are provided for illustration purpose only. They shouldn t be interpreted as an endorsement of any specific vendors or products. 18

19 ADTTE BDS, Composite Endpoint (Ex. 3) Time to Progression Free Survival (PFS) USUBJID PARAM PARAMCD AVAL STARTDT ADT CNSR EVNTDESC Progression Free Survival (days) Progression Free Survival (days) Progression Free Survival (days) Progression Free Survival (days) Progression Free Survival (days) Progression Free Survival (days) PFS DOCUMENTED PROGRESSION PFS COMPLETED STUDY. CENSORED AT TIME OF LAST ASSESSMENT PFS NEW ANTI-CANCER THERAPY. CENSORED AT TIME OF LAST ASSESSMENT PFS EARLY DISCONTINUATION. CENSORED AT TIME OF LAST ASSESSMENT. PFS DEATH PFS NO BASELINE ASSESSMENT. CENSORED AT TIME OF RANDOMIZATION. 19

20 ADTTE BDS, Composite Endpoint (Ex. 4) Time to Progression Free Survival (PFS) w/ an Additional Column USUBJ ID PARAM Progression Free Survival (days) Progression Free Survival (days) Progression Free Survival (days) PARAM CD AVAL STARTDT ADT CNSR EVNTDESC CNSDTDSC PFS DOCUMENTED PROGRESSION PFS COMPLETED STUDY PFS NEW ANTI-CANCER THERAPY LAST RADIOLOGIC ASSESSMENT SHOWING NO PROGRESSION LAST RADIOLOGIC ASSESSMENT SHOWING NO PROGRESSION Progression Free Survival (days) PFS EARLY DISCONTINUATI ON LAST RADIOLOGIC ASSESSMENT SHOWING NO PROGRESSION Progression Free Survival (days) PFS DEATH Progression Free Survival (days) PFS NO BASELINE ASSESSMENT RANDOMIZATION 20

21 ADTTE Analysis Dataset Metadata Dataset Name Dataset Description Dataset Location Dataset Structure Key Variables of Dataset Class of Dataset Documentation ADTTE Data for the time to event analyses adtte.xpt One record per subject per parameter USUBJID, PARAMCD BDS ADTTE.SAS, SAP Section 10.1 Note: The name ADTTE for the TTE analysis dataset is used for illustration purpose. It should not be noted that this imply a required naming convention for the TTE analysis dataset. 21

22 What ADTTE Document Covered ADaM Metadata Analysis Dataset Metadata Commonly Used Variables in ADTTE TTE Analysis Result Displays Analysis Results Metadata ADTTE Examples Time to Death (A Single Endpoint) Time to PFS (A Composite Endpoint) Time to Hep B e Antigen Seroconversion (A Composite Endpoint) 22

23 What ADTTE Does Not Cover Recurrent Events not BDS 23

24 Acknowledgement ADTTE Subteam ADaM Team CDISC Standards Review Council Public Reviewers like YOU 24

25 ADaM ADAE Model Presenter: Deb Bauer Sr. Manager of Biostatistics at Sanofi Author(s): ADaM ADAE Subteam 25

26 Overview of ADAE New class of dataset ADAE* No need for PARAM, AVAL or AVALC. Occurrences are counted Dictionary is used for coding Source for ADAE is the AE and SUPPAE domains and ADSL ADAE is required if SDTM AE can t support all AE analyses

27 ADAE Structure At least one record per each AE recorded in SDTM AE Exception: Screen failures Additional rows may be added for per period/phase or per coding path Only if required for analysis Must be clearly documented in dataset and variable metadata If an additional structure is needed an additional analysis dataset may be created i.e. Time-to-event of AE of interest 27

28 Variable Naming Conventions SDTM AE/SUPPAE variable names Same name, same meaning, same values AE prefix with an N suffix if it s the numeric version Analysis version of SDTM AE variable prefix of A Use existing ADaM variables from the IG Ex. APERIOD, APHASE, ANLzzFL Follow the ADaMIG naming conventions for new variables 28

29 The Model: Identifier variables Code List / Variable Name Variable Label Type Controlled Terms Core CDISC Notes STUDYID Study Identifier Char Req AE.STUDYID USUBJID SUBJID Unique Subject Identifier Subject Identifier for the Study Char Req AE.USUBJID Char Perm ADSL.SUBJID SITEID Study Site Identifier Char Perm ADSL.SITEID AESEQ Sequence Number Num Req AE.AESEQ Required for traceability back to SDTM AE. 29

30 The Model: Dictionary Coding variables AETERM, AEBODSYS, AEDECOD are Required *CD variables are Permissible Recommended that all levels of MedDRA hierarchy be included Metadata for each coding variable should include dictionary and version 30

31 The Model: Timing variables Variable Name Variable Label Type ASTDT Analysis Start Date ASTDTM ASTDTF ASTDY Analysis Start Date/Time Analysis Start Date Imputation Flag Analysis Start Relative Day Code List / Controlled Terms Core CDISC Notes Num Cond Created from converting AE.AESTDTC from character ISO8601 format to numeric date format, applying imputation rules as specified in the SAP or metadata. Conditional on whether start date is pertinent for study and AE.AESTDTC is populated in SDTM. Num Cond Conditional on whether start date-time is pertinent for study and AE.AESTDTC with time is populated in SDTM. Char (DATEFL) Cond Conditional on whether any imputation is done for the start date. Num Cond Example derivation: ASTDT ADSL.TRTSDT + 1 if ASTDT TRTSDT, else ASTDT ADSL.TRTSDT if ASTDT < TRTSDT Conditional on whether analysis start relative day is pertinent to the study. ADURN AE Duration (N) Num Perm Derive from ASTDT (or ASTDTM) and AENDT (or AENDTM) ADURU AE Duration Units Char Cond Conditional on whether ADURN is included. APERIOD Period Num Perm The numeric value characterizing the period to which the record belongs. APERIOD C Period (C) Char Perm Text characterizing to which period the record belongs. One-to-one map to APERIOD. APHASE Phase Char Perm Example derivation: If ASTDT<ADSL.TRTSDT, then APHASE= RUN-IN Else if ASTDT > ADSL.TRTEDT + x days then APHASE= FOLLOW-UP, Else APHASE= TREATMENT. 31

32 The Model: Indicator variables Code List / Variable Name Variable Label Type Controlled Terms Core CDISC Notes TRTEMFL Treatment Char Y Cond Example derivation: Emergent If ADSL.TRTSDT ASTDT ADSL.TRTEDT + x days then Analysis Flag TRTEMFL= Y AETRTEM ANLzzFL PREFL Treatment Emergent Flag Analysis Record Flag zz Pre-treatment Flag The number x is defined by the sponsor and often incorporates the known half-life of the drug. Variable TRTEMFL is to be used for any analysis of treatment-emergent AEs. This variable is conditional on whether the concept of treatment emergent is a key feature of the AE analyses. Char (NY) Perm SUPPAE.QVAL where QNAM= AETRTEM. See the SDTMIG version [3] for more information. TRTEMFL may differ from AETRTEM due to different definitions, date imputation, and other analysis rules. Including AETRTEM in addition to TRTEMFL will add traceability. Char Y Cond The ANLzzFL flag is useful in many circumstances; an example is when there is more than one coding path included for analysis, in which case separate analysis flags could be used to denote primary coding path or the records used for analysis from each coding path. See the ADaMIG version 1 [2] for more information on this flag variable. This variable is conditional on whether analysis records flags are needed for analysis. Char Y Cond Example derivation: If ASTDT < ADSL.TRTSDT then PREFL= Y This variable is conditional on whether the concept of pre-treatment AEs is a feature of the study and whether used for analysis. FUPFL Follow-up Flag Char Y Cond Example derivation: If ASTDT > ADSL.TRTEDT then FUPFL= Y This variable is conditional on whether the concept of follow-up AEs is a feature of the study and whether used for analysis. 32

33 The Model: Occurrence Flags Variable Name Variable Label Type AOCCFL 1st Occurrence of Any AE Flag AOCCSFL AOCCPFL AOCCIFL AOCCSIFL AOCCPIFL AOCCzzFL 1st Occurrence of SOC Flag 1st Occurrence of Preferred Term Flag 1st Max Sev./Int. Occurrence Flag 1st Max Sev./Int. Occur Within SOC Flag 1st Max Sev./Int. Occur Within PT Flag 1st Occurrence of. Code List / Controlled Terms Core CDISC Notes Char Y Perm Example derivation: Sort the data in the required order and flag the first treatment emergent record for each subject. Char Y Perm Example derivation: Sort the data in the required order and flag the first treatment emergent record for each body system for each subject. Char Y Perm Example derivation: Sort the data in the required order and flag the first treatment emergent record for each preferred term for each subject. Char Y Perm Example derivation: Sort the data in the required order and flag the first treatment emergent record for maximum severity for each subject. Char Y Perm Example derivation: Sort the data in the required order and flag the first treatment emergent record for maximum severity within body system for each subject. Char Y Perm Example derivation: Sort the data in the required order and flag the first treatment emergent record for maximum severity within preferred term for each subject. Char Y Perm Additional flag variables as needed for analysis. Derivation rules for these flags need to be described in the metadata. 33

34 The Model: Treatment/dose variables Treatment variables used for analyses must be included (TRTP, TRTA, TRTxxP, TRTxxA). Variable Name Variable Label Type DOSEAEON Study Drug Dose at AE Onset Code List / Controlled Terms Core CDISC Notes Num Perm Study drug dose a subject took when adverse event occurred. Example derivation: Obtained from EX.EXDOSE where AESTDTC falls between the values of EX.EXSTDTC and EX.EXENDTC DOSAEONU Study Drug Dose at AE Onset Units Char Cond Conditional on whether DOSEAEON is included. DOSECUM Cumulative Study Drug Dose Num Perm Cumulative study drug dose at the start of the AE. DOSECUMU Cumulative Study Drug Dose Units Char Cond Conditional on whether DOSECUM is included. 34

35 The Model: Descriptive variables SDTM AE variables Ex. AESER, AESEV, AEREL, AEACN, AETOXGR Numeric variables and analysis variables Ex. AESEVN, ASEV, ASEVN Pooled grouping variables Ex. SEVGRy, SEVGRyN 35

36 The Model: MedDRA Query variables Variable Name Variable Label Type Code List / Controlled Terms Core CDISC Notes SMQzzNAM SMQ zz Name Char Cond The standardized MedDRA query s name. Would be blank for terms that are not in the SMQ. Therefore this variable could be blank if none of the terms within the SMQ are present in the dataset. Conditional on whether SMQ analysis is done. SMQzzCD SMQ zz Code Num Perm The standardized MedDRA query s number code. SMQzzSC SMQ zz Scope Char BROAD, NARROW SMQzzSCN CQzzNAM SMQ zz Scope (N) Customized Query zz Name Cond The search strategy for SMQs can be narrow or broad. The preferred terms that are narrow in scope have high specificity for identifying events of interest while the broad terms have high sensitivity. By definition, all narrow terms are also considered within the broad score. Therefore, to summarize all broad terms, terms with either narrow OR broad would be considered. Will be null for terms that do not meet the criteria. Conditional on whether SMQ analysis is done. Num 1, 2 Perm Will be null for terms that do not meet the criteria. Char Cond The customized query (CQ) name or name of the AE of special interest category based on a grouping of MedDRA terms. Would be blank for terms that are not in the CQ. Conditional on whether CQ analysis is done. Examples: DERMATOLOGICAL EVENTS, CARDIAC EVENTS, IARS (INFUSION ASSOCIATED REACTIONS) 36

37 The Model: Original or Prior Coding variables Variable Name Variable Label Type DECDORGy PT in Original Dictionary y BDSYORGy HLGTORGy HLTORGy LLTORGy LLTNORGy SOC in Original Dictionary y HLGT in Original Dictionary y HLT in Original Dictionary y LLT in Original Dictionary y LLT Code in Original Dictionary y Code List / Controlled Terms Core CDISC Notes Char MedDRA Perm Original preferred term coding of AE.AETERM using MedDRA or other dictionary version X.X. Char MedDRA Perm Original body system coding of AE.AETERM using MedDRA or other dictionary version X.X. Char MedDRA Perm Original HLGT coding of AE.AETERM using MedDRA or other dictionary version X.X. Char MedDRA Perm Original HLT coding of AE.AETERM using MedDRA or other dictionary version X.X. Char MedDRA Perm Original LLT coding of AE.AETERM using MedDRA or other dictionary version X.X. Char MedDRA Perm Original LLT code of AE.AETERM using MedDRA or other dictionary version X.X. 37

38 Example 1: TEAE analyses Summary of Treatment Emergent Adverse Events by System Organ Class and Preferred Term Analysis Population: Safety SYSTEM ORGAN CLASS Preferred Term Treatment A (N=xxx) n (%) Treatment B (N=xxx) n (%) Number of subjects reporting at least one adverse event x (x.x) x (x.x) BLOOD AND LYMPHATIC SYSTEM DISORDERS At least one event x (x.x) x (x.x) Anaemia x (x.x) x (x.x) x (x.x) x (x.x) CARDIAC DISORDERS At least one event x (x.x) x (x.x) Angina pectoris x (x.x) x (x.x) x (x.x) x (x.x) <Other SOCs and PTs> N = Safety subjects, i.e., subjects who received at least one dose of study drug n = Number of subjects reporting at least one treatment emergent adverse event % = n / N * 100 Adverse events are presented by descending frequency within Treatment B System organ classes and preferred terms are coded using MedDRA version x.x. 38

39 Example 1: TEAE analyses (2) Dataset Name Variable Name Variable Label Variable Type Display Format Codelist / Controlled Terms Source / Derivation ADAE USUBJID Unique Subject Identifier text $11 AE.USUBJID ADAE TRTEMFL Treatment Emergent Analysis Flag ADAE ASTDT Analysis Start Date integer yymmdd 10. text $1 Y If ADSL.TRTSDT <= ASTDT<=(ADSL.TRTEDT +14) then TRTEMFL= Y <Sponsor will insert derivation here> ADAE ASTDTF Analysis Start Date Imputation Flag ADAE AEREL Causality text $25 NOT RELATED ADAE RELGR1 Pooled Causality Group 1 ADAE AOCCFL 1st Occurrence of Any AE Flag text $1 (DATEFL) If start date is completely missing or missing the year then ASTDTF= Y UNLIKELY RELATED POSSIBLY RELATED PROBABLY RELATED DEFINITELY RELATED text $25 Not Related Related ADAE TRTA Actual Treatment text $6 Drug A Else if start date has month missing then ASTDTF= M Else if start date has day missing then ASTDTF= D AE.AEREL If AE.AEREL is equal to NOT RELATED or UNLIKELY RELATED then RELGR1= Not Related Else if AE.AEREL is equal to POSSIBLY RELATED or PROBABLY RELATED or DEFINITELY RELATED or Causality is missing then RELGR1= Related text $1 Y Subset ADAE to Treatment Emergent Adverse Events (TRTEMFL= Y ) Drug B Sort by Subject (USUBJID), Analysis Start Date (ASTDT), and Sequence Number (AESEQ) and flag the first record (set AOCCFL= Y ) within each Subject ADSL.TRT01A 39

40 Example 2: Hemorrhages SMQ by sex Summary of Haemorrhages (SMQ) (Narrow scope) by Sex and Actual Treatment Group Analysis Population: Safety Mosaic Plot of Hemmorrhagic (SMQ) Preferred Terms by Sex and Actual Treatment Group Analysis Population: Safety 40

41 Example 2: Hemorrhages SMQ by sex (2) SMQ variable metadata 01 indicates this is the first SMQ Scope variables are included but for this SMQ only includes narrow scope terms Dataset Name Variable Name Variable Label Variable Type Display Format Codelist / Controlled Terms Source / Derivation ADAE SMQ01CD SMQ 01 Code integer 8.0 SMQ01CD= if the AEPTCD is included in this SMQ. ADAE SMQ01NAM SMQ 01 Name Text $200 SMQ01NAM= Haemorrhage terms (excl laboratory terms) (SMQ) if the AEPTCD is included in this SMQ. ADAE SMQ01SC SMQ 01 Scope Text $6 BROAD, NARROW For this given SMQ, all scopes are Narrow. ADAE SMQ01SCN SMQ 01 Scope (N) integer 1.0 1, 2 Map SMQ01SC to SMQ01SCN in the following manner: Broad = 1 Narrow = 2. 41

42 Example 3: AE analysis by severity and cumulative dose Summary of cumulative dose quartiles to first onset for PSN by severity grade Analysis population: Intent-to-treat 42

43 Example 3: AE analysis by severity and cumulative dose (2) Variable metadata Dataset Name Variable Name Variable Label Variable Type ADAE USUBJID Unique Subject Identifier ADAE ITTFL Intent-to-Treat Population Flag ADAE AEDECOD Dictionary-Derived Term ADAE AETOXGR Standard Toxicity Grade ADAE AETOXGRN Standard Toxicity Grade (N) ADAE DOSECUM Cumulative Study Drug Dose ADAE DOSECUMU Cumulative Study Drug Dose Units ADAE DOSCMGR1 Cumulative Dose Group 1 Display Format Codelist / Controlled Terms Source / Derivation Text $7 ADSL.USUBJID Text $1 Y,N ADSL.ITTFL Text $200 MedDRA AE.AEDECOD Text $25 1, 2, 3, 4, 5 AE.AETOXGR integer 1.0 1, 2, 3, 4, 5 Code AE.AETOXGR to numeric Float 6.2 Total all values of EX.EXDOSE for the subject up to the start of the AE. Text $2 mg EX.EXDOSU integer $12 Quartile 1 Quartile 2 Quartile 3 Quartile 4 Missing if DOSECUM=0, else DOSCMGR1 = Quartile 1 if DOSECUM is in the 1 st Quartile, Quartile 2 if in the 2 nd Quartile, Quartile 3 if in the 3 rd Quartile and Quartile 4 if in the 4 th Quartile. 43

44 Example 4: Cross-over example Three period cross-over (Treatment A, B, and combination A+B) 7 day treatment period and 7 day wash-out TEAE definition start of treatment period up 72 hours after end of treatment period ADAE sample data: USUBJID TRTA USUBJID AEDECOD ASTDY TRTEMFL PREFL FUPFL EPOCH APHASE APERIOD A VOMITING 5 Y FIRST TREATMENT FIRST TREATMENT B PHARYNGITIS 16 Y SECOND TREATMENT SECOND TREATMENT A+B HEADACHE 32 Y THIRD TREATMENT THIRD TREATMENT A+B CONSTIPATION 33 Y THIRD TREATMENT THIRD TREATMENT ORAL HERPES 38 Y FOLLOW-UP FOLLOW-UP HYPOTENSION 26 Y SECOND WASHOUT SECOND WASHOUT A+B 3 HEADACHE 28 Y THIRD TREATMENT THIRD TREATMENT A+B 3 HEADACHE 34 Y FOLLOW-UP THIRD TREATMENT 3 44

45 Strength through collaboration CDISC Tuberculosis Therapeutic Area Standard Update Presentation by: Jane Diefenbach, PharmaStat Authors: Jane Diefenbach (PharmaStat) and Bess LeRoy (C-Path)

46 TB: Global Unmet Medical Need TB Burden and Impact 2 billion people or approximately 1/3 of the world s population is infected with TB TB kills 3,800 people every day and 1 person every 25 seconds 9.4 million new cases annually TB is 1 of the 3 greatest causes of death for women ages TB is the leading cause of death amongst people with HIV/AIDS Cases of MDR and XDR are increasing 46

47 TB: Global Unmet Medical Need Treatment for active, drug sensitive TB consists of 4 (first line) medicines WHO requires the use of combination to prevent resistance Have significant drug interactions and adverse effects Require prolonged treatment (6-9months) Not compatible with many HIV therapies Nearly five decades old New drug development tools are urgently needed 47

48 TB: The Challenge High unmet need, but low return in investment Pre-clinical and clinical tools have advanced since that time, but not uniformly applied to TB drug development Complexity of disease state(s) Active/latent MDR/XDR Co-infection with HIV Requirement for combination therapy 48

49 Challenges to TB Drug Co-Development Need for more efficacious and faster acting drugs Difficulty in selecting most appropriate combinations Need to determine the appropriate doses of each drug Challenge of assessing the contribution of individual drugs in the combination for both efficacy and safety Need to develop novel development plans and regulatory strategies 2 recent draft guidance documents by FDA: Co-development and Drug Development tools 49

50 Critical Path to TB Drug Regimens A collaboration to accelerate the development of new, safe, and highly effective regimens for TB by enabling early testing of drug combinations. 50

51 What CPTR Does CPTR funds research and provides a collaborative environment. CPTR join to share. Key CPTR efforts: Advance Biomarkers and Endpoints Advance Disease Progression Models Advance Preclinical and Clinical Sciences Facilitate interaction with Health Authorities Publish Data Standards and Integrate Data (DSI)

52 Overview of TB Therapeutic Area Standard 52

53 Components needed to implement the new TB Standards Study Data Tabulation Model (SDTM) and Implementation Guide Documents - Provide a general framework for describing the organization of information collected during a study Controlled Terminology - Standardized terms and definitions Tuberculosis SDTM User Guide - Data examples important to TB shown in SDTM format 53

54 SDTM Domains (Old structures) Interventions Con Meds Exposure Substance Use Trial Design Trial Elements Trial Arms Trial Visits Trial Incl/Excl Trial Summary Events Adverse Events Disposition Medical History Deviations Clinical Events Microbiology Spec. Microbiology Suscept. Findings ECG Incl/Excl Exceptions Labs Physical Exam Questionnaire Subject Characteristics Vital Signs Drug Accountability Findings About PK Concentrations PK Parameters Special Purpose Demographics Comments Subject Elements Subject Visits Relationships SUPPQUAL RELREC 54

55 Draft SDTM Domains (New structures) Findings DU (Device in Use) MI (Microscopic Findings) MO (Morphology) SR (Skin Response) 55

56 Controlled Terminology The TB terminology that is out for public review includes.. Additions to code lists that already exist. - METHOD is an existing code list; ELISPOT and WESTERN BLOT are new additions. New code lists to capture new terms that currently do not fit into existing code lists. - New code list called DRUG RESISTANCE STATUS containing the terms SUSCEPTIBLE, MULTIPLE DRUG RESISTANCE, and EXTENSIVE DRUG RESISTANCE. 56

57 TB terminology out for public review 57

58 Tuberculosis SDTM User Guide 58

59 Tuberculosis SDTM User Guide Shows examples of how to structure data that are important to TB and are not covered by the SDTM implementation guide. Needs to be used in conjunction with the STDM and controlled terminology. Demonstrates the controlled terminology. Will be an evolving document to include new concepts and examples. 59

60 Skin Response (SR) Clinical Events (CE) Exposure (EX) Data Element: Phase of TB Treatment Data Element: TB Symptoms Data Element: Tuberculin Skin Test Result Definition: The number of millimeters in diameter of the induration, or raised hardening, at the tuberculin skin test site. Permissible value set: mm of induration. How did the data elements become examples in the user guide? ~139 Data Elements Model to CDISC domains CDISC Domains Tuberculosis SDTM User Guide Examples compiled into user guide USUBJID EXTRT EXDOS EXDOSU USUBJID CETERM CEPRESP CEOCCUR USUBJID SRTESTCD SRTEST SRORRES SRORRESU INDURDIA Induration Diameter 16 mm 60

61 HIV antibody test case report form example HIV 1/2 Antibody Test Results Subject ID: ABC Sample Collection Date: HIV 1/2 antibody test done: Yes X No Sample Type: BLOOD Testing Method: ELISA Result: POSITIVE 61

62 HIV antibody test case report form example (cont d) HIV 1/2 Antibody Test Results Subject ID: ABC Sample Collection Date: HIV 1/2 antibody test done: Yes X No Sample Type: BLOOD Testing Method: WESTERN BLOT Result: POSITIVE 62

63 HIV antibody test data in SDTM format Data modeled in LB domain USUBJID LBTESTCD LBTEST LBORRES LBSPEC LBMETHOD LBDTC ABC HIV12AB HIV-1/2 Antibody POSITIVE BLOOD ELISA ABC HIV12AB HIV-1/2 Antibody POSITIVE BLOOD WESTERN BLOT

64 HIV antibody test example in the user guide 64

65 What types of examples are shown in the user guide? Prior medications including TB treatment and vaccines. Also current non-study medication Vaccine injection site reaction Prior diagnosis of TB, HIV infection, and other events Chest x-ray and other imaging data 65

66 Concluding remarks The SDTM implementation guide, controlled terminology document, and TB SDTM user guide should be used together. The TB STDM user guide and associated new terminology include examples and terms that are important for TB. The TB user guide will continue to evolve. 66

67 All documents are publically available SDTM TB Terminology TB User Guide Published Controlled Terminology ibrary/terminologyresources/cdisc 67

68 Q&A Panel 68

69 CDISC Education and Communication Information 69

70 Read our CDISC Latest News Join our list and Read Our Monthly e-newsletter; See the CDISC home page: Read our Blogs: Review Prior Monthly Newsletters: Attend our Japan and U.S. Interchanges in July and October Join our Social Media 70

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72 Current Opportunities Standards for Review and Comment Tuberculosis Standard (comments due 8 June) Volunteers being sought: XML Aficionados Technical Writer Protocol Representation Device Terminology Specialist Introduction to CDISC Book Available for $15.00 Discount for Device companies to join CDISC 72

73 2012 CDISC Public Training Events Hosted by Jazz Pharmaceuticals in Palo Alto, CA from June Registration has closed; contact Saad Yousef if you are interested in registering for this public training. Spots still open for ADaM, CDASH, and SDTM courses! CDISC Interchange in Japan (Tokyo) from July 2012 Hosted by Synteract in Morrisville, NC from August Hosted by Business & Decision in Brussels, Belgium from 3-6 September CDISC International Interchange (Baltimore, MD) from October* Visit to register Early registration discounts available for select public training events 73

74 CDISC Private Training Any organization can request a private, in-house training at their location. Visit our website to submit a request for more information A CDISC staff member will respond to your inquiry 1-2 days after your request has been submitted. 74

75 Upcoming CDISC Webinars Watch for more SDS Team Updates on our Webinar in June Date and link to event will be provided at a later time. CDISC Webinar info located at 75

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