{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T08:06:24Z","timestamp":1791446784757,"version":"4.3.3"},"reference-count":49,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2019,10,29]],"date-time":"2019-10-29T00:00:00Z","timestamp":1572307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>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.<\/jats:p>","DOI":"10.1093\/biomet\/asz057","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T15:30:14Z","timestamp":1568388614000},"source":"Crossref","is-referenced-by-count":3,"title":["Semiparametric estimation of structural failure time models in continuous-time processes"],"prefix":"10.1093","author":[{"given":"S","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, North Carolina, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K","family":"Pieper","sequence":"additional","affiliation":[{"name":"Duke Clinical Research Institute, Duke University, 300 W. Morgan Street, Durham, North Carolina, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F","family":"Cools","sequence":"additional","affiliation":[{"name":"Department of Cardiology, AZ Klina, Augustijnslei 100, 2930 Brasschaat, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,10,29]]},"reference":[{"key":"2019103013355275500_B1","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-4348-9","volume-title":"Statistical Models Based on Counting Processes","author":"Andersen,","year":"1993"},{"key":"2019103013355275500_B2","volume-title":"An Introduction to Numerical Analysis","author":"Atkinson,","year":"1989"},{"key":"2019103013355275500_B3","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1111\/j.1541-0420.2005.00377.x","article-title":"Doubly robust estimation in missing data and causal inference models","volume":"61","author":"Bang,","year":"2005","journal-title":"Biometrics"},{"key":"2019103013355275500_B4","volume-title":"Efficient and Adaptive Inference in Semiparametric Models","author":"Bickel,","year":"1993"},{"key":"2019103013355275500_B5","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1093\/biomet\/asp033","article-title":"Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data","volume":"96","author":"Cao,","year":"2009","journal-title":"Biometrika"},{"key":"2019103013355275500_B6","volume-title":"Analysis of Survival Data","author":"Cox,","year":"1984"},{"key":"2019103013355275500_B7","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1002\/sim.5686","article-title":"Methods for dealing with time-dependent confounding","volume":"32","author":"Daniel,","year":"2013","journal-title":"Statist. 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