Semiparametric estimation of structural failure time models in continuous-time processes.

ABSTRACT: Structural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been proposed for estimating the model parameters in the presence of time-dependent confounding and administrative censoring. However, most existing methods require manually pre-processing data into regularly spaced data, which may invalidate the subsequent causal analysis. Moreover, the computation and inference are challenging due to the nonsmoothness of artificial censoring. We propose a class of continuous-time structural failure time models that respects the continuous-time nature of the underlying data processes. Under a martingale condition of no unmeasured confounding, we show that the model parameters are identifiab

SUBMITTER: Yang S 

PROVIDER: S-EPMC7646189 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature