SAS JMP Statistical Discovery Clinical 2024 Free Download

Introduction

SAS JMP Statistical Discovery Clinical 2024 is a specialized desktop software suite from SAS Institute. Tailored for clinical research and healthcare analytics it delivers interactive graphics robust statistical modeling clinical trial monitoring and automated patient reporting—all within a user-friendly interface 

Features

• Comprehensive statistical tools for clinical trial data analysis data integrity checks survival analysis and advanced modeling
• Automated patient profiles & narratives—reducing manual reporting workload and facilitating regulatory compliance
• Data visualization & dashboards: interactive graphical outputs and summary panels for safety and quality monitoring 
• CDISC/SDTM conversion tools: import raw clinical datasets and transform to standard formats for submission
• Customizable reporting with templates to streamline repeated analysis 
• Integration with SAS, R, MATLAB etc.—leveraging JMP’s scripting language (JSL) for automation 

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Technical Setup Details

• Installer: Offline standalone package (~728 MB file JMP_Clinical_18.rar) for Windows (32‑bit & 64‑bit) 
• Release date: July 8–9, 2024
• Supported languages: English Japanese Simplified Chinese (multi-language pack) 
• Architecture: x64 Windows only 

System Requirements

According to GetIntoPC (July 2024):

• OS: Windows 7/8/10 (32 or 64‑bit)
• CPU: Intel Dual‑Core or higher 
• RAM: 2 GB minimum (though JMP docs recommend 8–16 GB for clinical modules) 
• Disk Space: 1 GB minimum—again real-world usage often needs 10–15 GB+

Note: SAS/JMP’s official clinical edition often assumes:

• CPU: Pentium 4+ RAM: 8 GB+ Disk: 128 GB+

Your real-world needs may be higher depending on dataset size and complexity.

Conclusion

SAS JMP Statistical Discovery Clinical 2024 is a powerful analytics platform for clinical professionals and researchers. It combines:

• Rich statistical analysis and CDISC/SDTM conversion
• Automated reporting
• Interactive dashboards and
• Integration with broader SAS/R ecosystems.

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