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CDISC's CDASH and SDTM: Why You Need Both!
- 1. CDAS HMissing Data Absence of evidence is not evidence of absence: must check that missing data is missing CDASH Show me the data, not lack of data SDTM SDTM assumes that if no record exists then nothing happened. This only works if it was checked in data capture, which requires a question and record (e.g., Were Any AEs Experienced?). Subject 1023 exists in CDASH, but as they had no AEs, they are not in SDTM. Rationale In typical data capture, each question has an answer list (codelist). If the codelist is different, the question may need a different variable*. SDTM models questionnaires “vertically,” with one variable for the question and one for the answer. When codelists vary by question, vertical data can only be done after data collection. Each question with a different codelist of answers may have to be in a different variable* CDASH Findings data, like surveys, must be in a vertical structure, with all answers in one variable name SDTM “Findings” Data Data on each CRF is driven by what is captured together, not necessarily by domain CDASH Data must be organized into datasets by domain SDTM Each CRF should have the data that makes sense to collect together. Per CDASH, this can mix domains if standard variable names are used, and in the end the data appear in the right domains. In SDTM, data MUST appear only in the correct domain. Data Organization Premise: • CDASH and SDTM are very similar but the differences are critical. • 89% of CDASH maps directly to SDTM variables with standard mappings included in the CDASH Implementation Guide v2.0 (e.g., dates) • 11% are different for a reason SDTM is optimized for tabulation, analysis dataset creation, & data submission. CDASH is optimized for data capture, investigator site activities, & data quality. Different requirements lead to different philosophies, but with same end in mind. CDAS H USUBJID AETERM AESTDTC AEENDTC 2006-34-1022 HEADACHE 2016-10-14 2016-10-20 2006-34-1022 RASH 2016-10-14 2016-10-21 2006-34-1024 TIRED 2016-09-24 2016-09 2006-34-1024 HEADACHE 2016-09-24 2016-09-24 SDTM Authorship: • Kit Howard, Senior Education Expert, CDISC, khoward@cdisc.org • Shannon Labout, Data Sciences & Clinical Data Management, Roivant Sciences (formerly CDISC), shannon.labout@gmail.com • With acknowledgement and thanks to Gary Walker, Associate Director, SDTM Center of Excellence, IQVIA Philosophy RationalePhilosophy *Depends upon the data capture system CDASH SUBJID HAMD101 HAMD104 HAMD105 1 Absent No difficulty falling asleep No difficulty USUBJID RSTESTCD RSTEST RSORRES 2324-P0001 HAMD101 HAMD1-Depressed Mood Absent 2324-P0001 HAMD104 HAMD1-Insomnia Early - Early Night No difficulty falling asleep RationalePhilosophy CDASH DM CDASH RP CDASH SDTM USUBJID SUBJID BRTHDTC SEX RACE ABC12 01 1948-12-13 M WHITE ABC12 02 1955-03-22 M BLACK USUBJID RPTESTCD RPORRES RPSTRESC ABC12 MENOSTAT Premenarchal Premenarchal DM SDTM RP SDTM SDTM USUBJID RSTESTCD RSTEST RSORRES 2324-P0001 HAMD101 HAMD1-Depressed Mood Absent 2324-P0001 HAMD104 HAMD1-Insomnia Early - Early Night No difficulty falling asleep Requires absolute predictability of the data to maximize machine-readability Facilitates consistent data entry interface without constraining system characteristics CDASHSDTM Human- vs Machine- Readable Data SDTM data must be absolutely predictable to be efficiently usable by many parties (CROs, regulatory agencies, data warehouses, etc.); it’s why SDTM is basically a “flat file”. But… data capture systems vary a lot. CDASH allows for variability (paper, EDC, PRO, etc.) while including the structure and mapping necessary to get to SDTM. CDASH dd-mon-yyyy hh:mm:ss Regulatory agencies Data warehouses Sponsors/CROs/Vendors RationalePhilosophyDate Example SDTM yyyy-mm-ddThh:mm:ss ©CDISC2018