Personalized treatment selection using observational data

Abstract

Estimating the optimal treatment regime based on individual patient characteristics has been a topic of discussion in many forums. Advanced computational power has added momentum to this discussion over the last two decades and practitioners have been advocating the use of new methods in determining the best treatment. Treatments that are geared toward the ‘best’ outcome for a patient based on his/her genetic markers and characteristics are of high importance. In this article, we develop an approach to predict the optimal personalized treatment based on observational data. We have used inverse probability of treatment weighted machine learning methods to obtain score functions to predict the optimal treatment. Extensive simulation studies showed that our proposed method has desirable performance in selecting the optimal treatment. We provided a case study to examine the Statin use on cognitive function to illustrate the use of our proposed method.

Keywords:

• Observational studies
• design variables
• personalized treatments
• propensity scores

2020 Mathematics Subject Classification:

• 62G05

Acknowledgments

This work was conducted in part using the resources of the University of Louisville's research computing group and the Cardinal Research Cluster.

Disclosure statement

No potential conflict of interest was reported by the author(s).