Our experience in data assembly is extensive: we’ve been building analysis-ready datasets for pharmacometric modeling and simulation for almost 30 years. Importantly, we have also been continuously refining and improving our processes. In a recent 5-year period we wrote over 40,000 data assembly programs. We take pride in the comprehensive preparation we do and the efficient process we’ve built for data assembly and creation of analysis-ready datasets. To ensure a thorough understanding of data assembly needs to support the planned pharmacokinetic and pharmacodynamic modeling, we undertake a complete review of the Investigator’s Brochure, protocols, case report forms, study reports, and relevant literature. Data assembly and QC activities are based on our established standard operating procedures. We know very well that the validity of analysis results depends on the quality and accuracy of the constructed analysis-ready datasets.
Data Assembly Standards
We recognize the importance of clear and unambiguous data assembly requirements. We have systematically examined the requirements and standard practices across multitudes of projects in order to build critical functionality into an elegant framework that supports pharmacometric data and scientific workflows. Our data programmers have a toolkit full of standard requirement forms, custom macros, coding templates, and code snippets to expedite the conversion time and ensure the quality of the analysis-ready data. And we continue to improve upon these standards with every new project.
Serving as an Unblinded Provider in a Blinded Study
Our teams have strict policies and procedures in place that support our service as unblinded data reviewers during the execution of blinded trials. Working within this system allows us to perform critical data programming tasks and analyses so that data and modeling results can be available for top-line results.
The quality of our analysis-ready datasets is widely recognized by our clients. Rigorous quality checking is performed throughout the data assembly process to ensure the dataset was built correctly. Quality activities include:
- In-process programming checks to assure data integrity and anticipated program functionality.
- Independent comparison of the data back to source data to ensure the accuracy of data transformations as specified in the requirements.
- Scientific assessment to confirm that the data are adequate for the planned analyses.
- Utilization of diagnostic visuals generated from our standard Graph Library to uncover potential data anomalies
Contact us today to find out how our high-quality data assembly & support services can help you achieve your project goals!
By Darcy Hitchcock, Director of Quality and Data Management for Cognigen