TreatmentPatterns: An R package to facilitate the standardized development and analysis of treatment patterns across disease domains. (October 2022) - Markus, Aniek F.; Verhamme, Katia M.C.; Kors, Jan A.; Rijnbeek, Peter R.
Highlights: TreatmentPatterns R package for development and analysis of treatment patterns. Formally defines and implements the process of constructing treatment pathways. Accessible, standardized, and interpretation friendly tool for a broad audience. Supports the accumulation of knowledge on treatment patterns across disease domains. Example study in Dutch IPCI database to demonstrate functionalities of the package. Abstract: Background and objectives: There is an increasing interest to use real-world data to illustrate how patients with specific medical conditions are treated in real life. Insight in the current treatment practices helps to improve and tailor patient care, but is often held back by a lack of data interoperability and a high-level of required resources. We aimed to provide an easy tool that overcomes these barriers to support the standardized development and analysis of treatment patterns for a wide variety of medical conditions. Methods: We formally defined the process of constructing treatment pathways and implemented this in an open-source R package TreatmentPatterns (https://github.com/mi-erasmusmc/TreatmentPatterns ) to enable a reproducible and timely analysis of treatment patterns. Results: The developed package supports the analysis of treatment patterns of a study population of interest. We demonstrate the functionality of the package by analyzing the treatment patterns of three common chronic diseases (type II diabetes mellitus, hypertension, and depression) in the Dutch Integrated Primary Care Information (IPCI) database. Conclusion: TreatmentPatterns is a tool to make the analysis of treatment patterns more accessible, more standardized, and more interpretation friendly. We hope it thereby contributes to the accumulation of knowledge on real-world treatment patterns across disease domains. We encourage researchers to further adjust and add custom analysis to the R package based on their research needs. Graphical abstract: Image, graphical abstract