Presentation on theme: "The Importance of CDASH"— Presentation transcript:
1The Importance of CDASH Benjamin Vali, M.S.Mathematical StatisticianDivision of Biometrics IIICDER/OTS/OBU.S. Food and Drug Administration
2FDA DisclaimerThis presentation reflects the views of the authors and should not be construed to represent FDA’s views or policies.
3Outline What do we do? What do we need? What do we get? CDASH NDA/BLA Statistical Review of EfficacyWhat do we need?All Regulatory Reviewers at FDA/CDERWhat do we get?Current ApproachesCDASHFocusing on The Best
4What do we do? Statistical Review of Efficacy for NDA/BLA Regulation Citation21 CFR....Reports of adequate and well-controlled investigations provide the primary basis for determining whether there is "substantial evidence" to support the claims of effectiveness for new drugs.
5What do we do? Statistical Review of Efficacy for NDA/BLA These “adequate and well-controlled investigations” pertain to the “Pivotal/Confirmatory” trials (i.e. Phase 3 trials) in a clinical development programConsequently all aspects of these protocols and corresponding Statistical Analysis Plans (SAPs) need to be pre-specifiedincluding how the clinical data are captured
6What do we do? Statistical Review of Efficacy for NDA/BLA Key Analysis Questions to ask during Marketing Application Review:Are the key results correct?Programmatically validate all major results during the filing review - IMPORTANT!
7What do we do? Statistical Review of Efficacy for NDA/BLA Programmatically validate all major results during the filing reviewReviewer needsProtocolSAPClinical Study Report (CSR)Annotated case report form (aCRF)Tabulation/clinical datasets with corresponding metadata/data definition file (SDTM, define.xml)Analysis datasets with corresponding metadata/data definition file (ADaM, define.xml)Reviewers’ guide (e.g., SDRG & ADRG)
8What do we do? Statistical Review of Efficacy for NDA/BLA Programmatically validate all major results during the filing reviewTwo-step processIndependent quality validation of Analysis Dataset VariablesPrimary and Key Secondary EndpointsAdditional key subject-level analysis variables (e.g. analysis set flags)Independent quality validation of Analysis ResultsInferential and/or Descriptive
9What do we do? Statistical Review of Efficacy for NDA/BLA Key Analysis Questions to ask during Marketing Application Review:Are the results consistent?Similar findings from each clinical studyNot sensitive to different approaches to analysisSimilar across study subgroupsSupported by results from secondary endpoints (which are generally related to primary endpoints)Conduct further analyses with support and insight from Medical Officer
10What do we need? All Regulatory Reviewers at FDA/CDER MetadataTraceabilityData Standards
11Why Push for Traceability? Full transparency for each data point throughout (end-to-end) clinical data lifecycle (from the source “to my computer”)Need to make sure that the data were captured and analyzed in a way that is consistent with what was pre-specified in the protocol and SAP!
12The Clinical Data Lifecycle Source Documents Case Report Form (CRF) Clinical Database Analysis Database Tables, Listings, and Figures (TLFs) Clinical Study Report (CSR) Product Labeling Promotional Materials
13Why Push for Data Standards? Overall Improved Efficiency Analyzing DataConsistent data structureConsistent nomenclatureAnalysis-readyStandardize not only the dataMetadataRepresentation of the relationship between data elementsCommunicationSDRG and ADRG
14Traceability vs. Data Standards MY Regulatory Reviewer PerspectiveTraceability is keyTraceability may be even more important than Data Standards (To Me)
15And I know it is important to you… Inherent in this principle is a need for traceability to allow an understanding of where an analysis value (whether an analysis result or an analysis variable) came from, i.e., the data’s lineage or relationship between an analysis value and its predecessor(s).CDISC ADaM v2.1….
16Traceability vs. Data Standards What we often see submitted by ApplicantsIn the creation of standard data for submissions, traceability is (at times), compromised by post-data-capture mapping
22Approach #5 (Getting There …) … Legacy CRF Clinical Database SDTM ADaM TLFs …
23Approach #6 (The Best)… CDASH CRF SDTM ADaM TLFs …
24CDASH CDASH - Clinical Data Acquisition Standards Harmonization Best facilitates mapping to SDTM and creation of high quality CRF pagesClosest to 1-to-1 mapping in terms of structure, content and formatPages for 16 out of 25 major SDTM domains are coveredBest Practices for creating other domain pages
25CDASH Gets it right the first time Done at data capture stage!Eliminates the need for more downstream workStreamlines everything on the Production and Regulatory Review sidesStandards development instituted as early as possible within the clinical data lifecycleCDASH is the best example of how effective this can be
26CDASHThe original premise, per DIA Meeting in January 2006, was for efficiencyMotivated by ACRO and FDA – Critical PathCurrent Version 1.1 – January 18, 2011We’ve come a long way since previous versions/previous eraVersion 2.0 release imminentWill begin development of therapeutic area specific CRFs
27As We Move Forward…Don’t map for the sake of mapping (i.e., from legacy datasets to SDTM and ADaM)Approaches #2, #3 and #4Unnecessary cost (time and money) to applicantsIncludes time and money wasted for responding to subsequent Information Requests during the review cycle!Strive for Approach #6 – no mapping required!
28As We Move Forward… The principle behind this approach is key More so than the CDASH standard itselfImplementation is everything!More so than the content standards in general themselvesMust adhere to CDISC foundational principles
29As We Move Forward…Right now we are living during a “transitional” period (i.e., from legacy to standardized data), hence you need to do what you need to doDon’t pull the e-brake while going at 150 mphThis doesn’t have to happen todayJust eventually…