Post-lock Data Flow: From CRF to FDA
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The document outlines the data flow from clinical trial data to submission to the FDA, detailing the steps involved in extracting and processing raw data into analysis datasets. It emphasizes the importance of documentation and adherence to data standards, specifically the Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM), to ensure compliance for submissions. It highlights the differences between wide and tall dataset formats and the efficiency gains from using standardized documentation and structures, while also noting challenges in legacy data conversions.
Post-lock Data Flow: From CRF to FDA
- 1. Post-Lock Data Flow: From CRF to FDA BenVaughn, MS, RAC Principal Statistical Scientist
- 2. Broad Strokes of What Statisticians Do with ClinicalTrial Data Get data from data management Do assorted “Stuff” with that data MakeTables, Listings and Figures TransferTables, Listings and Figures to MedicalWriting
- 3. A little more detail and some terms -Step 1: EDC System Data • Data exported from an EDC System is frequently called “raw” data. – Can contain superfluous variables (edit checks, audit trails, etc.) – Not very conducive to generating tables (form follows function: typically parallels CRF pages) – Documentation isn’t designed for an FDA reviewer as the audience – Datasets are highly variable from vendor to vendor and from various EDC systems – Generally speaking there is a 1:1 relationship; each data point appears once and only once in the raw data
- 4. A little more detail and some terms -Step 2: Analysis datasets • Datasets generated from the raw data to facilitate the production ofTables Listings and Figures are called “Analysis Datasets” (ADS) – Any variables that must be derived are added: scoring of instruments, determination of whether AEs are treatment emergent, determination of baseline values and calculation of change from baseline, etc. – Key variables are merged onto all records: treatment codes, covariates, study start and stop dates, age, gender, race, etc. – Datasets might be split or combined into more logical groups; many different patient reported outcomes might be in a single dataset from DM, but have different analysis rules, and therefore be split into multiples analysis datasets. – Goal is to create datasets where most key points of information on tables can be generated with one procedure
- 5. The Assorted “Stuff” • Documentation is written for the raw EDC data to allow an FDA reviewer to understand the source of each data point • Analysis datasets (ADS) are generated using (SAS) programs to transform the raw data into analysis datasets; these can be rerun on new cuts of data • Documentation is written for the ADS to allow an FDA reviewer to understand the source of each variable/row and how it maps from the raw data • Did I mention documentation? FDA loves documentation.
- 6. But wait! Data standards Electronic Standardized Study DataTimeline (Fitzmartin, PhUSE 2014)
- 7. Data Standards, Cont. • ALL data submitted to FDA for studies starting next year, MUST conform to data standards (but sponsors should already be doing it) • These guidances are BINDING, refusal to file is possible if they are not followed • A draft guidance defines what the standards are: Study Data Tabulation Model (SDTM) for the “raw” data andAnalysis Data Model (ADaM) for the analysis datasets; this guidance is actively reviewed and updated • A sponsor may apply for a waiver, but FDA seems unlikely to grant them
- 8. Data Standards: SDTM • Extremely rigid format • Anything can be mapped into this format, and there is a standard for expanding it for things that don’t map well to an existing pre-specified dataset • Does not necessarily reflect the flow of a clinic visit or the CRF design, which can make it difficult to implement directly in an EDC system • Some types of data there is no excuse not to get in SDTM from the start (ex: central labs vendor should be able to provide SDTM data) • Standardized documentation (define.xml) • Submitted to FDA in place of raw data
- 9. Data Standards: ADaM • Typically uses SDTM as its source • Somewhat less rigid than SDTM • Fewer specified data structures (but expanding): – ADSL (Subject- Level dataset; standard variables for treatments, dates, sites, age, sex, race, populations – ADAE (Adverse Events) – ADTTE (Time to event) – OCCDS (Occurrence Data Structure, generalization of ADAE for things like Medical History and Concomitant Medications) – BDS (Everything else) • Standardized documentation (Define.xml or Define.pdf)
- 10. Data Standards: ADaM, cont. Legacy data is frequently a “Wide” format… Subject Visit DIABP SYSBP PULSE RESP WEIGHT HEIGHT BMI DIABPB L SYSBP BL PULSEBL RESPBL WEIGHTB L HEIGHTBL BMIBL DIABPCB L SYSBPCB L PULSEC BL RESPCB L WEIGHTCB L HEIGHTCB L BMICB L
- 11. Data Standards: ADaM, cont. Crammed onto one row: Subjec t Visit DIABP SYSBP PULSE RESP WEIG HT HEIGH T BMI DIABP BL SYSBP BL PULSE BL RESPB L WEIG HTBL HEIGH TBL BMIBL DIABP CBL SYSBP CBL PULSE CBL RESPC BL WEIG HTCBL HEIGH TCBL BMICB L
- 12. Data Standards: ADaM, cont. SDTM and ADaM are “Tall, Skinny” formats SUBJID AVISIT N PARAMCD AVAL BASE CHG 001 1 DIABP 001 1 SYSBP 001 1 PULSE 001 1 RESP 001 1 WEIGHT 001 1 HEIGHT 001 1 BMI
- 13. Data Standards: ADaM Advantages • Huge efficiencies for table programming: – You almost never need to look up variable names – Programming code for one table can be altered to make a similar table by just changing the dataset and parameters • Standard documentation allows reviewers to easily understand what is in each dataset, how it was derived and which flags should be used to produce a particular display • Data from multiple studies can be “Stacked” as long as things like the parameter codes are uniform
- 14. Data Standards: ADaM Disadvantages • Datasets are a bigger investment • Completely fails where you need multiple outcomes on a single row • “Drill down” questions are problematic; can be created as additional rows/ outcomes, but clinical reviewers are typically interested in how they relate to the questions that triggered the drill down • Clinical reviewers almost always want “Wide” listings: Everything collected at the same time point on a single row (Transpose of the data is required)
- 15. CDASH: Related, but not required • Clinical Data Acquisition Standards Harmonization (CDASH) is a suite of standardized CRFs and variable names for the data points collected in those forms • Goes cleanly and uniformly into SDTM • Saves time and money! • Your study is no longer a unique snowflake • It is likely that there will always be non-standard data collected, so manual mapping will be required
- 16. Broad Strokes of What Statisticians Do Get data from data management Map “raw” data to SDTM and generate documentation Map SDTM data to ADaM and generate documentation MakeTables, Listings and Figures TransferTables, Listings and Figures to MedicalWriting
- 17. NDA/BLA Submission • An integrated analysis of safety and efficacy (ISS/ISE) will be needed for nearly all NDAs and BLAs • Many individual studies must be combined into an ISS/ISE database • Integrated data must be summarized in ISS/ISE post-text tables • This is distilled into the ISS/ISE text and sections 2.7.3 and 2.7.4 of the eCTD
- 18. Ideal Dataflow Process CDASH CRF data SDTM ADaM TLFs CSR Study 2 ADS Study …nADS Study 3 ADS ISS/ ISE ADS ISS ISE TLFs ISS ISE All SDTM is created consistently; study analysis datasets are created with uniform structures; all information can be cleanly and sequentially linked back to the CRF data. NDA
- 19. MoreTypical State of Data CRF data SDTM Study ADS (ADaM) TLFs CSR (Some) Phase III studies CRF data in Legacy Format Study ADS TLFs CSR Phase I/II (III) studies Assorted judgment calls with documentation of varying quality
- 20. Considerations for Legacy Conversions • FDA places an extremely high value on traceability and reproducibility- this trumps any data standard • SDTM conversion of legacy data is NOT required • When converting legacy data to SDTM for submission (where CSRs were generated off legacy data) FDA suggests additionally submitting the legacy data • FDA has not clearly indicated that it uses SDTM data in any way for non-pivotal trials where the CSR relies on legacy data.
- 21. Suggested Integration and Submission Approach SDTM Study #1 Study ADS (ADaM) TLFs CSR Legacy Study #1 Study ADS TLFs CSR SDTM Study #2..n Study ADS (ADaM) TLFs CSR Legacy Study #2..n Study ADS TLFs CSR Map study ADS into uniform ADAM ISS ISE TLFs ISS ISE NDA
- 22. References • SDTM and ADaM specs and implementation: http://www.cdisc.org/ • Study Data Technical Conformance Guide: http://www.fda.gov/downloads/ForIndustry/DataStandards/St udyDataStandards/UCM384744.pdf • eStudy Data Guidance: http://www.fda.gov/downloads/Drugs/GuidanceComplianceR egulatoryInformation/Guidances/UCM292334.pdf