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

Abstract Number:

3443 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Christina Zhou (1), Nikki Freeman (1), Michael Kosorok (1)

Institutions:

(1) University of North Carolina at Chapel Hill, Chapel Hill, NC

Co-Author(s):

Nikki Freeman  
University of North Carolina at Chapel Hill
Michael Kosorok  
University of North Carolina at Chapel Hill

First Author:

Christina Zhou  
University of North Carolina at Chapel Hill

Presenting Author:

Christina Zhou  
N/A

Abstract Text:

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.

Keywords:

Precision medicine|Individualized treatment rule|Competing risk|Survival analysis|Random forest|

Sponsors:

Biometrics Section

Tracks:

Personalized/Precision Medicine

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