ArticleBiometrika2020
Semiparametric estimation of structural failure time models in continuous-time processes.
Article in Biometrika, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Multiply-robust estimator of cumulative incidence function difference for right-censored competing risks data.BMC medical research methodology · 2026Article
- Statistical inference for heterogeneous treatment effect with right-censored data from synthesizing randomized clinical trials and real-world data.Biometrics · 2025Article
- Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding.Lifetime data analysis · 2025Article
- Continuous-Time Causal Inference With Marked Point Process Weights: An Example on Sodium-Glucose Co-Transporters 2 Inhibitor Medications and Urinary Tract Infection.Statistics in medicine · 2025Article
- Variable selection for doubly robust causal inference.Statistics and its interface · 2025Article
- Article
- Estimating spatially varying health effects of wildland fire smoke using mobile health data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations.Biometrics · 2024Article
- Estimating the causal effects of multiple intermittent treatments with application to COVID-19.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations.Biometrics · 2022Article
- A treatment-specific marginal structural Cox model for the effect of treatment discontinuation.Pharmaceutical statistics · 2022Article
- Propensity Score Modeling in Electronic Health Records with Time-to-Event Endpoints: Application to Kidney Transplantation.Journal of data science : JDS · 2022Article
- Robust inference of conditional average treatment effects using dimension reduction.Statistica Sinica · 2022Article
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Authors and funding
3 authors.
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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.
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