sdtm-oak

Convert raw EDC/eCRF clinical data into CDISC SDTM domains.

93|23|Updated Mar 4, 2026

Search Tags:#data management #clinical trials #data conversion #CDISC #SDTM

Ready to run

npx skills add https://github.com/RConsortium/pharma-skills --skill sdtm-oak

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sdtm-oak
Source: https://github.com/RConsortium/pharma-skills/tree/main/sdtm-oak
Command: npx skills add https://github.com/RConsortium/pharma-skills --skill sdtm-oak

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sdtm.oak, dplyr, tibble, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex process of converting raw EDC/eCRF data into structured CDISC SDTM domains, saving time and reducing errors in clinical trial data management.

Core Features & Use Cases

Automatic SDTM Domain Derivation: Converts raw clinical data into SDTM-compliant formats for events, interventions, findings, and supplements.
Controlled Terminology Recoding: Applies CDISC controlled terminology to recode variable values, ensuring consistency and compliance.
Date Derivation and Formatting: Converts raw date/time data into ISO 8601 format for accurate analysis and reporting.
Use Case: A clinical researcher uses this Skill to quickly generate SDTM datasets from raw EDC data, simplifying the process of preparing data for regulatory submissions.

Quick Start

Derive the SDTM AE domain from the raw AE data file 'ae_raw.csv' using the sdtm-oak skill.

Frequently Asked Questions about sdtm-oak

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert raw EDC data into CDISC SDTM domains?▼

You can convert raw EDC data into CDISC SDTM domains by applying automated domain derivation, controlled terminology recoding, and ISO 8601 date formatting to structure events, interventions, and findings for regulatory submissions.

What is the best way to apply CDISC controlled terminology recoding to clinical trial data?▼

Applying CDISC controlled terminology recoding maps raw variable values to standardized codes, ensuring consistency and compliance across SDTM domains. This automated recoding process eliminates manual mapping errors in clinical trial datasets.

Do I need dplyr and tibble to format clinical trial dates into ISO 8601?▼

Yes, you need dplyr and tibble packages along with sdtm.oak to process raw date and time data. These dependencies handle the data manipulation required to convert clinical trial dates into ISO 8601 format for accurate analysis.

Can I automate SDTM domain derivation for all CDISC domain types like findings and interventions?▼

Yes, you can automate SDTM domain derivation for events, interventions, findings, and supplements. The process converts raw eCRF clinical data into structured SDTM-compliant formats using controlled terminology and domain-specific derivations.

What are the limitations of using sdtm.oak for clinical data conversion?▼

The sdtm.oak conversion process requires raw clinical datasets and controlled terminology specifications as input, and depends on the dplyr and tibble R packages. It is specifically designed for EDC and eCRF source data formats rather than arbitrary data structures.