Services

Statistical Programming

  • SAS Programming: Develop and maintain SAS programs for statistical analysis, dataset creation, and data manipulation.

  • R Programming: Use R for statistical modeling, visualizations, and data handling in clinical trials.

  • Python: Provide programming support using Python for advanced data science applications, machine learning models, or automation in data processing.

  • Custom Reporting Tools: Develop custom scripts to automate the generation of Tables, Listings, and Figures (TLFs) for clinical trial reports.

Quality Control and Validation

  • Double Programming: Perform independent programming to validate datasets and analysis results, ensuring data integrity.

  • Validation of Statistical Code: Conduct validation checks on SAS, R, or Python code for accuracy, consistency, and reproducibility.

Clinical Data Visualization

  • Graphs and Plots: Create advanced data visualizations (e.g., Kaplan-Meier curves, forest plots, waterfall plots, spaghetti plots) to represent clinical trial data.

  • Interactive Dashboards: Develop interactive dashboards (using tools like Shiny in R or Power BI) for clinical teams to explore data in real-time.

Regulatory Submission Support

  • Submission Datasets: Prepare datasets and documentation in accordance with regulatory standards (e.g., CDISC-compliant datasets) for submissions to the FDA, EMA, or other regulatory agencies.

  • Integrated Summary of Safety (ISS) / Integrated Summary of Efficacy (ISE): Create datasets and perform analyses required for the ISS and ISE sections of the New Drug Application (NDA) or Biologics License Application (BLA).

  • Reviewer’s Guide and Define.XML: Prepare essential documentation such as the Reviewer’s Guide, Define.XML, and annotated CRFs (Case Report Forms) for regulatory submissions.

Training and Support

  • Programming Workshops: Provide training sessions or workshops in SAS, R, Python, or CDISC standards for clinical and statistical teams.

  • Ongoing Statistical Support: Offer ongoing support for clinical research teams during the trial lifecycle, including troubleshooting, re-analysis, and data interpretation.