Context:

One of the clients was running a cancer trial that generated large, complex datasets from multiple hospitals, including tumor measurements, treatment cycles, and survival outcomes. Manual data cleaning and analysis caused delays, inconsistencies, and difficulty applying standardized criteria like RECIST. To improve this situation our team was selected.

Resolution:

We deployed a specialized team with deep knowledge of oncology trials and Real-World Data analytics. They implemented an R based workflow using packages such as tidy verse, survival, and R Markdown. R automated data cleaning, calculated tumor response, performed survival analysis, and generated reproducible reports for trial monitoring and regulatory submission.

Result:

Data processing time dropped significantly, tumor response evaluation became more accurate, and survival analysis was completed faster. Reporting that once taking weeks was reduced to hours, improving decision making and overall trial efficiency.