Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models

Abstract Background In many applications of instrumental variable (IV) methods, the treatments of interest are intrinsically time-varying and outcomes of interest are failure time outcomes. A common example is Mendelian randomization (MR), which uses genetic variants as proposed IVs. In this article...

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Autores principales:	Joy Shi, Sonja A. Swanson, Peter Kraft, Bernard Rosner, Immaculata De Vivo, Miguel A. Hernán
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Mendelian randomization Instrumental variable Structural nested models G-estimation Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/15930bd45cd94f2cac6994e68340dfff
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spelling	oai:doaj.org-article:15930bd45cd94f2cac6994e68340dfff2021-11-28T12:38:40ZInstrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models10.1186/s12874-021-01449-w1471-2288https://doaj.org/article/15930bd45cd94f2cac6994e68340dfff2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12874-021-01449-whttps://doaj.org/toc/1471-2288Abstract Background In many applications of instrumental variable (IV) methods, the treatments of interest are intrinsically time-varying and outcomes of interest are failure time outcomes. A common example is Mendelian randomization (MR), which uses genetic variants as proposed IVs. In this article, we present a novel application of g-estimation of structural nested cumulative failure models (SNCFTMs), which can accommodate multiple measures of a time-varying treatment when modelling a failure time outcome in an IV analysis. Methods A SNCFTM models the ratio of two conditional mean counterfactual outcomes at time k under two treatment strategies which differ only at an earlier time m. These models can be extended to accommodate inverse probability of censoring weights, and can be applied to case-control data. We also describe how the g-estimates of the SNCFTM parameters can be used to calculate marginal cumulative risks under nondynamic treatment strategies. We examine the performance of this method using simulated data, and present an application of these models by conducting an MR study of alcohol intake and endometrial cancer using longitudinal observational data from the Nurses’ Health Study. Results Our simulations found that estimates from SNCFTMs which used an IV approach were similar to those obtained from SNCFTMs which adjusted for confounders, and similar to those obtained from the g-formula approach when the outcome was rare. In our data application, the cumulative risk of endometrial cancer from age 45 to age 72 under the “never drink” strategy (4.0%) was similar to that under the “always ½ drink per day” strategy (4.3%). Conclusions SNCFTMs can be used to conduct MR and other IV analyses with time-varying treatments and failure time outcomes.Joy ShiSonja A. SwansonPeter KraftBernard RosnerImmaculata De VivoMiguel A. HernánBMCarticleMendelian randomizationInstrumental variableStructural nested modelsG-estimationMedicine (General)R5-920ENBMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mendelian randomization Instrumental variable Structural nested models G-estimation Medicine (General) R5-920
spellingShingle	Mendelian randomization Instrumental variable Structural nested models G-estimation Medicine (General) R5-920 Joy Shi Sonja A. Swanson Peter Kraft Bernard Rosner Immaculata De Vivo Miguel A. Hernán Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models
description	Abstract Background In many applications of instrumental variable (IV) methods, the treatments of interest are intrinsically time-varying and outcomes of interest are failure time outcomes. A common example is Mendelian randomization (MR), which uses genetic variants as proposed IVs. In this article, we present a novel application of g-estimation of structural nested cumulative failure models (SNCFTMs), which can accommodate multiple measures of a time-varying treatment when modelling a failure time outcome in an IV analysis. Methods A SNCFTM models the ratio of two conditional mean counterfactual outcomes at time k under two treatment strategies which differ only at an earlier time m. These models can be extended to accommodate inverse probability of censoring weights, and can be applied to case-control data. We also describe how the g-estimates of the SNCFTM parameters can be used to calculate marginal cumulative risks under nondynamic treatment strategies. We examine the performance of this method using simulated data, and present an application of these models by conducting an MR study of alcohol intake and endometrial cancer using longitudinal observational data from the Nurses’ Health Study. Results Our simulations found that estimates from SNCFTMs which used an IV approach were similar to those obtained from SNCFTMs which adjusted for confounders, and similar to those obtained from the g-formula approach when the outcome was rare. In our data application, the cumulative risk of endometrial cancer from age 45 to age 72 under the “never drink” strategy (4.0%) was similar to that under the “always ½ drink per day” strategy (4.3%). Conclusions SNCFTMs can be used to conduct MR and other IV analyses with time-varying treatments and failure time outcomes.
format	article
author	Joy Shi Sonja A. Swanson Peter Kraft Bernard Rosner Immaculata De Vivo Miguel A. Hernán
author_facet	Joy Shi Sonja A. Swanson Peter Kraft Bernard Rosner Immaculata De Vivo Miguel A. Hernán
author_sort	Joy Shi
title	Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models
title_short	Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models
title_full	Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models
title_fullStr	Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models
title_full_unstemmed	Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models
title_sort	instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models
publisher	BMC
publishDate	2021
url	https://doaj.org/article/15930bd45cd94f2cac6994e68340dfff
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Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models

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