Defining Treatment Regimens and Lines of Therapy Using Real-World Data in Oncology
Lisa M. Hess, Xiaohong Li, Yixun Wu, Robert Goodloe, Zhanglin Lin Cui
Abstract
Retrospective observational research relies on databases that do not routinely record lines of therapy or reasons for treatment change. Standardized approaches to estimate lines of therapy were developed and evaluated in this study. A number of rules were developed, assumptions varied and macros developed to apply to large datasets. Results were investigated in an iterative process to refine line of therapy algorithms in three different cancers (lung, colorectal and gastric). Three primary factors were evaluated and included in the estimation of lines of therapy in oncology: defining a treatment regimen, addition/removal of drugs and gap periods. Algorithms and associated Statistical Analysis Software (SAS®) macros for line of therapy identification are provided to facilitate and standardize the use of real-world databases for oncology research.