Causal Diagram Techniques for Urologic Oncology Research

Large quantities of data are now available to medical researchers; however, observational studies are plagued by bias and confounding. Additionally, much of this research only speculates on variable associations, leaving prospective randomized clinical trials as the sole purveyors of claims about causal relations between variables. There has been a growing movement of causal inference that uses new techniques to investigate causality using observational data. These techniques include the implementation of directed acyclic graphs, which allow researchers to explicitly and reproducibly define the causal relationships between study variables, thus making statistical analysis more robust. Directed acyclic graphs further allow researchers to identify confounding and other sources of bias and to discover causal effects among complex networks of variables. This review aims to introduce these techniques to the general urology and urologic oncology research communities in order to provide a basic understanding of causal inference and analysis and call for integration of these practices more generally in research methodology.