Derive ADaM BDS datasets from SDTM findings domains using admiral.
npx skills add https://github.com/RConsortium/pharma-skills --skill admiral-bds
Please help me install this Agent Skill. Skill: admiral-bds Source: https://github.com/RConsortium/pharma-skills/tree/main/admiral/admiral-bds Command: npx skills add https://github.com/RConsortium/pharma-skills --skill admiral-bds
This Skill automates the derivation of ADaM Basic Data Structure (BDS) datasets from SDTM findings domains, streamlining the process of creating and maintaining clinical trial data.
Use the admiral-bds skill to derive the ADVS and ADLB datasets from the provided SDTM VS and LB domains.
High-intent search queries and answers about installing and using this skill.
ADaM BDS datasets are derived from SDTM findings domains by applying admiral routines to transform VS and LB data into ADVS and ADLB structures, ensuring CDISC compliance for analysis and reporting.
ADaM Basic Data Structure (BDS) is used for clinical trial data analysis and reporting, providing a standardized dataset format derived from SDTM findings to support regulatory submissions.
Yes, a completed ADSL dataset is required before deriving ADaM BDS datasets. The derivation process needs SDTM input data, an ADaM BDS specification, and ADSL to generate ADVS and ADLB.
Yes, admiral automates the generation of ADVS and ADLB datasets from SDTM VS and LB domains. It integrates with dplyr and lubridate to streamline the ADaM BDS derivation process.
This ADaM derivation approach ensures datasets adhere to CDISC ADaM standards for quality and regulatory compliance by systematically transforming SDTM findings into BDS structures.
Using admiral for BDS data derivation requires SDTM input data, an ADaM BDS specification, and a completed ADSL dataset. It is specifically designed for findings domains like VS and LB rather than all SDTM domains.
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