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

TitleSemiparametric estimation of structural failure time models in continuous-time processes.
Publication TypeJournal Article
Year of Publication2020
AuthorsYang, S, K Pieper, and F Cools
JournalBiometrika
Volume107
Issue1
Pagination123-136
Date Published2020 Mar
ISSN0006-3444
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 identifiable from a potentially infinite number of estimating equations. Using the semiparametric efficiency theory, we derive the first semiparametric doubly robust estimators, which are consistent if the model for the treatment process or the failure time model, but not necessarily both, is correctly specified. Moreover, we propose using inverse probability of censoring weighting to deal with dependent censoring. In contrast to artificial censoring, our weighting strategy does not introduce nonsmoothness in estimation and ensures that resampling methods can be used for inference.

DOI10.1093/biomet/asz057
Alternate JournalBiometrika
Original PublicationSemiparametric estimation of structural failure time models in continuous-time processes.
PubMed ID33162561
PubMed Central IDPMC7646189
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States