🧪 Clinical Data Standards Lab

A hands‑on, end‑to‑end clinical laboratory for mastering modern CDISC standards — including Biomedical Concepts (BCs), Dataset Specializations, SDTM, ADaM, Dataset‑JSON, CORE, and ARS — through a structured, portfolio‑ready learning program.

This repository documents my journey to deepen expertise in clinical data standards and build a complete CDISC implementation from scratch.

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🎯 Purpose of This Repository

The Clinical Data Standards Lab is designed to:

• Build a full, realistic CDISC workflow
• Practice advanced SDTM and ADaM engineering
• Explore emerging standards (BCs, Dataset‑JSON, ARS)
• Gain hands‑on experience with CORE rule writing and validation
• Create reusable metadata and automation patterns
• Prepare a strong portfolio for senior/principal programming roles

This is a learning lab, not a production environment — experimentation is encouraged.

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🗂️ Repository Structure

clinical-data-standards-lab/
│
├── 01_biomedical_concepts/
│   ├── bc_models/
│   ├── dataset_specializations/
│   └── curation/
├── 02_sdtm/
│   ├── mapping/
│   ├── domains/
│   ├── timing/
│   └── validation/
│
├── 03_adam/
│   ├── adsl/
│   ├── bds/
│   ├── tte/
│   └── metadata/
│
├── 04_dataset_json/
│   ├── conversions/
│   ├── json_examples/
│   └── ndjson/
│
├── 05_core/
│   ├── rules/
│   ├── rule_engine_runs/
│   └── validation_reports/
│
├── 06_ars/
│   ├── ars_metadata/
│   ├── tfl_designer/
│   └── automation_examples/
│
├── 07_integration_project/
│   ├── sdtm/
│   ├── adam/
│   ├── json/
│   ├── core/
│   └── ars/
│
└── README.md

Each folder aligns with a major CDISC standard or milestone.

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📅 Roadmap

A structured, modern CDISC learning path — in the correct order.

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Chapter 1: Biomedical Concepts & Dataset Specializations

What you learn

• BC modeling
• Dataset Specialization creation
• Curation principles

Why this chapter comes first

This chapter establishes the semantic foundation of the entire CDISC stack.
Instead of starting with SDTM tables, we begin with meaning‑first metadata:

• What is the clinical concept?
• What attributes define it?
• How is it represented consistently across studies?

Key Topics

• BC schema (concept_id, attributes, ontology references)
• Concept relationships
• Dataset Specialization design
• Curation principles (consistency, traceability, QC, documentation)

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Chapter 2: SDTM (Study Data Tabulation Model)

What you learn

• Concept‑to‑SDTM mapping
• Domain structures
• Timing variables
• SDTM validation

Why this chapter is second

SDTM is the first regulatory‑facing layer after semantic modeling.
It transforms BC‑driven meaning into standardized tabular structures.

Key Topics

• Mapping specifications
• Domain creation
• Timing alignment
• Validation outputs

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Chapter 3: ADaM (Analysis Data Model)

What you learn

• ADSL, BDS, TTE
• Intermediate datasets
• Complex derivations
• Metadata‑driven ADaM design

Why this chapter is third

ADaM builds directly on SDTM and supports statistical analysis and traceability.

Key Topics

• BDS criteria variables
• TFL‑driven dataset design
• Analysis metadata
• Reproducibility principles

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Chapter 4: Dataset‑JSON v1.1

What you learn

• JSON structure
• Conversion workflows
• NDJSON
• Error diagnosis

Why this chapter is fourth

Dataset‑JSON is the modern transport format replacing SAS XPT.
It sits between SDTM/ADaM and regulatory submission.

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Chapter 5: CORE (CDISC Open Rules Engine)

What you learn

• Writing CORE rules
• Running the rule engine
• Understanding conformance logic
• Validating SDTM & ADaM

Why this chapter is fifth

CORE validates SDTM, ADaM, and Dataset‑JSON — so it must come after them.

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Chapter 6: ARS (Analysis Results Standard)

What you learn

• ARS v1.0 model
• ARM support
• Metadata‑driven TFL representation
• Automation patterns

Why this chapter is sixth

ARS sits on top of ADaM and represents analysis results in machine‑readable form.

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Chapter 7: Integration Project

Build a mini‑submission package:

• SDTM
• ADaM
• Dataset‑JSON
• CORE validation
• ARS metadata
• BCs

This chapter brings everything together into a complete CDISC workflow.

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Chapter 8: Portfolio & Documentation

• Final documentation
• GitHub portfolio preparation
• Interview‑ready explanations

This chapter prepares your work for real‑world presentation.

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🧰 Tools & Technologies Used

• SAS (OnDemand or local)
• Python (Colab / Jupyter)
• JSON / NDJSON
• CDISC Library
• CORE (Open Rules Engine)
• Excel / CSV metadata templates

This project focuses on deterministic, metadata‑driven CDISC standards.

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📌 What This Repository Demonstrates

By the end of this project, the repository showcases:

• Advanced SDTM and ADaM engineering
• Metadata‑driven dataset design
• Dataset‑JSON conversions
• Custom CORE conformance rules
• Biomedical Concept modeling
• ARS metadata creation and automation
• A complete CDISC workflow:
  raw → SDTM → ADaM → JSON → CORE → ARS

This repository is intended for:

• Senior Statistical Programmers
• Principal Programmers
• Data Standards Leads

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🙌 Acknowledgments

Inspired by CDISC standards, open‑source tools, and the goal of building a modern, end‑to‑end understanding of clinical data engineering.