A Bayesian approach for dynamic treatment regimes in the presence of competing risk analysis

Abstract

Background: A sequencing rule is considered to formulate the dynamic treatment regime (DTR). This sequence rule is based on the clinical relevance, prior evidence about the best performing therapy, and the requirement to treat a patient in a specific scenario. The challenge occurs when the study offers a concluding remark about best effective therapy among all possible combinations of treatment management schedules treated with a sequence rule. The time-to-event data analysis is the only available method to figure out the best effective treatment in the context of oncology research. However, the presence of the competing risk event of death in the time-to-event analysis is unavoidable, and it becomes challenging to decide regarding the best effective treatment strategy. Methodology: In this article, we describe the statistical methodology to handle the competing risk time-to-event data analysis in DTR. The analysis is performed with the Bayesian approach to help determine the best effective treatment strategy. Results: We introduce the OpenBUGS function, which provides the comparison and estimation of different treatment sequences in time-to-event competing risk data analysis adopting the newly proposed statistical approach. Conclusion: This method is efficient to guide the personalized medicine in oncology setup through the supportive decision rule.

Keywords

• bayesian
• competing risk
• dynamic treatment regime
• personalized medicine
• sequential rule