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Advancements in ADaM Datasets for Effective Data Analysis and Communication

This presentation discusses the evolving landscape of ADaM datasets, focusing on standardization and automation to streamline data analysis in clinical research. Key insights include the benefits of clear communication about data, shared practices among reviewers and service providers, and the importance of aligning with the latest standards, such as SDTM. By examining practical examples and discussing the effective utilization of subject-level information, the presentation aims to highlight the critical role of ADaM in enhancing submission formats and ensuring analysis readiness.

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Advancements in ADaM Datasets for Effective Data Analysis and Communication

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  1. ADaM on a Diet Preventing Wide and Heavy ADs Dirk Van Krunckelsven Phuse 2011, Brighton

  2. Standard Data – Clear benefits • Easier automation / tools • Better communication about the data • Reviewers • Service Providers • Partners • Easier sharing and inheriting of work Presentation title in footer | 00 Month 0000

  3. SDTM and ADaM – Submission formats • ADaM datasets: • Analysis Ready • Focus on Key Results • Not every listing in a CTR • Use for other purposes than submission too • Work in (near) ADaM always • Some companies: ADs for all deliverables • Retrospective vs. Prospective Presentation title in footer | 00 Month 0000

  4. SDTM: Mature standard • Lots of standard domains available • Something does not fit? • Supplemental: --SUPP • New domain: Follow classification and pick • Event: --TERM • Intervention: --TRT • Finding: --TEST(CD) Presentation title in footer | 00 Month 0000

  5. SDTM and ADaM • SDTM • Model: version 1.2 • IG: version 3.1.2 • ADaM (Dec 2010) • Model: version 2.1 • IG: version 1.0 Presentation title in footer | 00 Month 0000

  6. ADaM: Two models, some drafts • ADSL – Subject Level Analysis Dataset • BDS – Basic Data Structure • Draft ADAE • Extend to General Occurrences AD • Draft ADTTE • Actually a case for BDS • Nice examples document out just now Presentation title in footer | 00 Month 0000

  7. ADaM: Info not described in the models • A lot of information not described in the model • Subject Level Information • Often ends up in ADSL • Additional variables • Often copied to all other analysis datasets • CDER common issues document • Though: ADs for all outputs • Bearing in mind: • ADaM ADs not only for submissions • ADs for all deliverables Presentation title in footer | 00 Month 0000

  8. ADaM: Info not described in the models • Baseline information • Height – Weight • BMI – Study specific, lab baselines • Categories of Baseline information • Discontinuation Reasons • Treatment • Study • Treatment Duration • Smoking, Drinking, other Risk Factors • durations – frequencies – … Presentation title in footer | 00 Month 0000

  9. Plug it all onto ADSL? • All such subject level information can go on ADSL • Naming convention to adhere to • What is still standard? • Good communication? • Very Wide ADSL • All other ADs become wide • If all copied over • Not necessarily all, what to choose? Presentation title in footer | 00 Month 0000

  10. HEIGHTBL HEIBLGR1 HEBLGR1N TRTDUR WEIGHTBL WEIBLGR1 WEBLGR1N DISSTREA BMIBL BMIBLGR1 BMIBLGR1 DISTRREA [LAB]BL [LAB]BL1 [LAB]BL1N [LAB]BL2 [LAB]BL2N OTHERS DISSTREA [LAB]BL BMIBL HEIGHTBL HEBLGR1N WEIGHTBL DISTTREA TRTDUR HEIBLGR1 OTHERS

  11. Can we standardize? • Yes, in structure • Use what we have available • BDS • Supplemental structure • Can standardize in content also • Gradually • Terminology • Apply Naming Convention as Terminology Presentation title in footer | 00 Month 0000

  12. ADSLSUPP • Additional “normalized” dataset: • ADSLSUPP: Supplemental Subject Level Information or • BDSL: Basic Data Subject Level • Same principle as Supplemental • Use BDS as model Presentation title in footer | 00 Month 0000

  13. ADSLSUPP Presentation title in footer | 00 Month 0000

  14. ADSLSUPP Presentation title in footer | 00 Month 0000

  15. ADSLSUPP Presentation title in footer | 00 Month 0000

  16. ADSLSUPP is Standardized Storage • Merge with other data is trivial • Subject Level  STUDYID USUBJID • See paper • All information readily available for • Output generation • Further exploration: sub setting, grouping, etc. • Submit also? • Reviewer may be interested as well… Presentation title in footer | 00 Month 0000

  17. Let’s talk ADaM! • CDISC ADaM team • More drafts, examples • Hard work • Volunteers • Reviewer Acceptance!? • SDTM for analyses? • Cf. Chuck Cooper’s Keynote presentation • Phuse 2011: SDTM (10) ADaM (6) Presentation title in footer | 00 Month 0000

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