Novel Use of Patient-Specific Covariates From Oncology Studies in the Era of Biomedical Data Science: A Review of Latest Methodologies

In this article, we review different applications of how to incorporate individual patient variables into clinical research within oncology. These methodologies range from the more traditional use of baseline covariates from randomized clinical trials, as well as observational studies, to using covariates to generalize the results of randomized clinical trials to other populations. Individual patient variables also allow for the consideration of heterogeneity in treatment effects and individualized treatment rules. We primarily consider two treatment groups and mostly focus on time-to-event outcomes where such methodologies have been well established and widely applied. We also discuss more conceptually newer statistical research that has not been widely applied in clinical oncology, but is likely to make an impact in future oncology research. With the increasing amount of biomedical data available for analysis, it is inevitable that more methods are developed to make best use of information, to advance oncology research.

Automated summary

This article reviews different applications of incorporating individual patient variables into clinical research in oncology. The methods discussed range from traditional use of baseline covariates to generalize results, to considering treatment effects heterogeneity and individualized treatment rules. The article also discusses newer statistical research that is likely to impact future oncology research.