Defining treatment regimens and lines of therapy using real-world data in oncology

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 (R)) macros for line of therapy identification are provided to facilitate and standardize the use of real-world databases for oncology research. Lay abstract: Most, if not all, real-world healthcare databases do not contain data explaining treatment changes, requiring that rules be applied to estimate when treatment changes may reflect advancement of underlying disease. This study investigated three tumor types (lung, colorectal and gastric cancer) to develop and provide rules that researchers can apply to real-world databases. The resulting algorithms and associated SAS (R) macros from this work are provided for use in the Supplementary Files.