数据集获取

git clone https://github.com/shailendraaher1/END-TO-END-Clinical-SAS-Project-Oncology-Raw-Data-CDISC-SDTM-ADaM-TLF-QC-Statistical-Programming.git

curl -L -o repo.zip https://github.com/shailendraaher1/END-TO-END-Clinical-SAS-Project-Oncology-Raw-Data-CDISC-SDTM-ADaM-TLF-QC-Statistical-Programming/archive/refs/heads/main.zip
unzip repo.zip

源站 README 摘录（使用方式）

🚀 "End-To-End Statistical Programming And Transformation Of Raw Data To CDISC Standards (SDTM ADaM,TLFs) For Synthetic Oncology Clinical Trial Data Using SAS OnDemand.

📋 Project Overview

“End-To-End Statistical Programming And Transformation Of Raw Data To CDISC Standards (SDTM, ADaM, TLFs) For Synthetic Oncology Clinical Trial Data Using SAS OnDemand®”
This project showcases a complete, industry-standard clinical data transformation workflow for a Phase III Oncology (Lung Cancer) clinical trial (ONCO-PEMBRO-500) using Base SAS and SAS OnDemand. It demonstrates end-to-end CDISC implementation from simulated raw data to SDTM mapping, ADaM analysis-ready datasets, and regulatory-compliant TLFs with QC validation.

🏛️ Academic & Institutional Details

• College: Dr. N. J. Paulbudhe College of Pharmacy, Savedi, Ahilyanagar, Maharashtra – 414003
• Affiliation: Savitribai Phule Pune University (SPPU), Pune, Maharashtra
• Academic Year: 2025 – 2026
• Program: M.Pharm (Pharmacology) - Semester IV
• Candidate: Mr. Aher Shailendra Shainath (Seat No.: 10492)
• Project Guide: Dr. Aman B. Upaganlawar (M.Pharm., Ph.D., M.B.A., FAMS, Department of Pharmacology)

🎯 Aim & Objectives

• Aim: To develop an end-to-end SAS Programming framework for the statistical analysis of synthetic oncology clinical data in compliance with CDISC Standards.
• Objectives:
  1. Import & Map: Raw Data to SDTM Standards.
  2. Derive: SDTM to ADaM Datasets for Analysis.
  3. Generate: Tables, Listings, and Figures (TLFs).
  4. Validate: Ensure code accuracy using Quality Control (QC).

🔬 Study Design & Protocol Comparison

• Protocol ID: ONCO-PEMBRO-500 (Simulated Advanced Scenario) vs. KEYNOTE-024 (NCT02142738 Original Reference)
• Study Phase: Phase III, Randomized, Double-Blind (Expanded for Power, $N = 500$)
  <br>
• Treatment Arms:
  • Arm A (Test): Pembrolizumab (200 mg) + Platinum Chemo
  • Arm B (Control): Placebo + Platinum Chemo
• Objective: SAS Programming & CDISC Implementation for Oncology Clinical Trials.

🧪 Why Synthetic Data?

• Privacy Compliance: Real patient data is strictly protected under HIPAA/GDPR laws and cannot be accessed for academic purposes.
• Technical Validation: Used High-Fidelity Synthetic Data to rigorously test complex SAS algorithms without ethical

数据加载示例（表格/文本类）

import pandas as pd, glob, os

files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))

完整仓库：github.com/shailendraaher1/END-TO-END-Clinical-SAS-Project-Oncology-Raw-Data-CDISC-SDTM-ADaM-TLF-QC-Statistical-Programming