Smooth semi-nonparametric (SNP) estimation of the cumulative incidence function.

ABSTRACT: This paper presents a novel approach to estimation of the cumulative incidence function in the presence of competing risks. The underlying statistical model is specified via a mixture factorization of the joint distribution of the event type and the time to the event. The time to event distributions conditional on the event type are modeled using smooth semi-nonparametric densities. One strength of this approach is that it can handle arbitrary censoring and truncation while relying on mild parametric assumptions. A stepwise forward algorithm for model estimation and adaptive selection of smooth semi-nonparametric polynomial degrees is presented, implemented in the statistical software R, evaluated in a sequence of simulation studies, and applied to data from a clinical trial in cryptococca

SUBMITTER: Duc AN 

PROVIDER: S-EPMC5518232 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature