A client conducting a multi-site cancer trial was struggling to manage data collected from different hospitals. Each site followed its own data entry standards and formats, making consolidation difficult. This created delays in analysis, compromised data quality, and complicated regulatory reporting.
We deployed a specialized team with deep expertise in oncology trials and Real-World Data (RWD) analytics. Using R, the team developed a structured workflow to import, clean, and standardize data from all participating centers. Data was ingested using packages like readr, readxl, and haven, and cleaned with janitor and validated using assertthat and custom logic. Standardization was handled with dplyr, and datasets were merged using bind_rows() and full_join(). Clinical coding was harmonized using lookup tables and recode() functions. For transparency and real-time monitoring, interactive dashboards were built with Shiny, and automated reports were generated using R Markdown.
The R-based solution streamlined the entire data management process across multiple sites. The final dataset was clean, consistent, and analysis-ready, enabling faster insights, smoother regulatory submissions, and better decision-making throughout the trial.
A client conducting a multi-site cancer trial was struggling to manage data collected from different hospitals. Each site followed its own data entry standards and formats, making consolidation difficult. This created delays in analysis, compromised data quality, and complicated regulatory reporting.
We deployed a specialized team with deep expertise in oncology trials and Real-World Data (RWD) analytics. Using R, the team developed a structured workflow to import, clean, and standardize data from all participating centers. Data was ingested using packages like readr, readxl, and haven, and cleaned with janitor and validated using assertthat and custom logic. Standardization was handled with dplyr, and datasets were merged using bind_rows() and full_join(). Clinical coding was harmonized using lookup tables and recode() functions. For transparency and real-time monitoring, interactive dashboards were built with Shiny, and automated reports were generated using R Markdown.
The R-based solution streamlined the entire data management process across multiple sites. The final dataset was clean, consistent, and analysis-ready, enabling faster insights, smoother regulatory submissions, and better decision-making throughout the trial.
Headquartered in New Jersey, Rang Technologies has dedicated over a decade delivering innovative solutions and best talent to help businesses get the most out of the latest technologies in their digital transformation journey. Rang Technologies has grown to become a global leader in Analytics, Data Science, Artificial Intelligence, Machine Learning, Salesforce CRM, Cloud, DevOps, Internet of Things (IoT), Cybersecurity, IT Consulting and Staffing, and Corporate Training. Our clients, which include Fortune 500 to Start-up companies, come from a wide array of industries, including pharmaceuticals, healthcare, retail, technology, BFSI, media, automobile, manufacturing, and several others. Our clients know they can rely on Rang Technologies to deliver customized and comprehensive digital solutions and talent to complement their business and technical objectives.
