Estimating optimal individualized treatment regimes for survival outcomes in competing risk data

Oregon Convention Center 

For more than a decade, the concept of using precision medicine (PM) to determine a patient's optimal treatment has gained popularity over the traditional "one-size-fits-all" treatment assignment based on covariate subgroup treatment effects. Extensive methodology for estimating individualized treatment regimes (ITRs) has been developed to account for individual heterogeneity. Although PM for survival data has become more abundant in recent years, there is less focus on estimating ITRs in the presence of competing risks (CR). CR are events where their occurrence precludes the occurrence of other events, and not accounting for them can lead to biased results. Because CR are prevalent in healthcare settings, we extend and develop nonparametric ITR estimation methodology using random survival forests into the CR setting. We propose a two-phase method that accounts for both overall survival of all events as well as cumulative incidence of the main event of interest. Simulation studies show that our proposed method works well, and we apply the proposed method to a cohort of peripheral artery disease patients.

Precision medicine

Individualized treatment rule

Competing risk

Survival analysis

Random forest 

Biometrics Section