title: Recursive Partitioning Method on Competing Risk Outcomes.
abstract: In some cancer clinical studies, researchers have interests to explore the risk factors associated with competing risk outcomes such as recurrence-free survival. We develop a novel recursive partitioning framework on competing risk data for both prognostic and predictive model constructions. We define specific splitting rules, pruning algorithm, and final tree selection algorithm for the competing risk tree models. This methodology is quite flexible that it can corporate both semiparametric method using Cox proportional hazards model and parametric competing risk model. Both prognostic and predictive tree models are developed to adjust for potential confounding factors. Extensive simulations show that our methods have well-controlled type I error and robust power performance. Finally, we apply both Cox proportional hazards model and flexible parametric model for prognostic tree development on a retrospective clinical study on oropharyngeal cancer patients.
authors.affiliation: Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada.; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
authors.forename: Wei
authors.initials: W
authors.lastname: Xu
authors.affiliation: Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada.; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
authors.forename: Jiahua
authors.initials: J
authors.lastname: Che
authors.affiliation: Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada.; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
authors.forename: Qin
authors.initials: Q
authors.lastname: Kong
keywords: Cox proportional hazards model; clinical cancer outcomes; competing risk outcomes; parametric competing risk model; prognostic and predictive effect; recursive partitioning algorithm; survival tree model