Estimation of separable direct and indirect effects in a continuous-time illness-death model
In this article we study the effect of a baseline exposure on a terminal time-to-event outcome either directly or mediated by the illness state of a continuous-time illness-death process with baseline covariates. We propose a definition of the corresponding direct and indirect effects using the conc...
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ftpubmed:oai:pubmedcentral.nih.gov:10764601 2024-02-04T10:00:56+01:00 Estimation of separable direct and indirect effects in a continuous-time illness-death model Breum, Marie Skov Munch, Anders Gerds, Thomas A. Martinussen, Torben 2023-06-04 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10764601/ http://www.ncbi.nlm.nih.gov/pubmed/37270750 https://doi.org/10.1007/s10985-023-09601-y en eng Springer US http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10764601/ http://www.ncbi.nlm.nih.gov/pubmed/37270750 http://dx.doi.org/10.1007/s10985-023-09601-y © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . Lifetime Data Anal Article Text 2023 ftpubmed https://doi.org/10.1007/s10985-023-09601-y 2024-01-07T02:06:23Z In this article we study the effect of a baseline exposure on a terminal time-to-event outcome either directly or mediated by the illness state of a continuous-time illness-death process with baseline covariates. We propose a definition of the corresponding direct and indirect effects using the concept of separable (interventionist) effects (Robins and Richardson in Causality and psychopathology: finding the determinants of disorders and their cures, Oxford University Press, 2011; Robins et al. in arXiv:2008.06019, 2021; Stensrud et al. in J Am Stat Assoc 117:175–183, 2022). Our proposal generalizes Martinussen and Stensrud (Biometrics 79:127–139, 2023) who consider similar causal estimands for disentangling the causal treatment effects on the event of interest and competing events in the standard continuous-time competing risk model. Unlike natural direct and indirect effects (Robins and Greenland in Epidemiology 3:143–155, 1992; Pearl in Proceedings of the seventeenth conference on uncertainty in artificial intelligence, Morgan Kaufmann, 2001) which are usually defined through manipulations of the mediator independently of the exposure (so-called cross-world interventions), separable direct and indirect effects are defined through interventions on different components of the exposure that exert their effects through distinct causal mechanisms. This approach allows us to define meaningful mediation targets even though the mediating event is truncated by the terminal event. We present the conditions for identifiability, which include some arguably restrictive structural assumptions on the treatment mechanism, and discuss when such assumptions are valid. The identifying functionals are used to construct plug-in estimators for the separable direct and indirect effects. We also present multiply robust and asymptotically efficient estimators based on the efficient influence functions. We verify the theoretical properties of the estimators in a simulation study, and we demonstrate the use of the estimators using data ... Text Greenland PubMed Central (PMC) Greenland Lifetime Data Analysis 30 1 143 180 |
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Article Breum, Marie Skov Munch, Anders Gerds, Thomas A. Martinussen, Torben Estimation of separable direct and indirect effects in a continuous-time illness-death model |
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Article |
description |
In this article we study the effect of a baseline exposure on a terminal time-to-event outcome either directly or mediated by the illness state of a continuous-time illness-death process with baseline covariates. We propose a definition of the corresponding direct and indirect effects using the concept of separable (interventionist) effects (Robins and Richardson in Causality and psychopathology: finding the determinants of disorders and their cures, Oxford University Press, 2011; Robins et al. in arXiv:2008.06019, 2021; Stensrud et al. in J Am Stat Assoc 117:175–183, 2022). Our proposal generalizes Martinussen and Stensrud (Biometrics 79:127–139, 2023) who consider similar causal estimands for disentangling the causal treatment effects on the event of interest and competing events in the standard continuous-time competing risk model. Unlike natural direct and indirect effects (Robins and Greenland in Epidemiology 3:143–155, 1992; Pearl in Proceedings of the seventeenth conference on uncertainty in artificial intelligence, Morgan Kaufmann, 2001) which are usually defined through manipulations of the mediator independently of the exposure (so-called cross-world interventions), separable direct and indirect effects are defined through interventions on different components of the exposure that exert their effects through distinct causal mechanisms. This approach allows us to define meaningful mediation targets even though the mediating event is truncated by the terminal event. We present the conditions for identifiability, which include some arguably restrictive structural assumptions on the treatment mechanism, and discuss when such assumptions are valid. The identifying functionals are used to construct plug-in estimators for the separable direct and indirect effects. We also present multiply robust and asymptotically efficient estimators based on the efficient influence functions. We verify the theoretical properties of the estimators in a simulation study, and we demonstrate the use of the estimators using data ... |
format |
Text |
author |
Breum, Marie Skov Munch, Anders Gerds, Thomas A. Martinussen, Torben |
author_facet |
Breum, Marie Skov Munch, Anders Gerds, Thomas A. Martinussen, Torben |
author_sort |
Breum, Marie Skov |
title |
Estimation of separable direct and indirect effects in a continuous-time illness-death model |
title_short |
Estimation of separable direct and indirect effects in a continuous-time illness-death model |
title_full |
Estimation of separable direct and indirect effects in a continuous-time illness-death model |
title_fullStr |
Estimation of separable direct and indirect effects in a continuous-time illness-death model |
title_full_unstemmed |
Estimation of separable direct and indirect effects in a continuous-time illness-death model |
title_sort |
estimation of separable direct and indirect effects in a continuous-time illness-death model |
publisher |
Springer US |
publishDate |
2023 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10764601/ http://www.ncbi.nlm.nih.gov/pubmed/37270750 https://doi.org/10.1007/s10985-023-09601-y |
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Greenland |
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Greenland |
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Greenland |
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Greenland |
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Lifetime Data Anal |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10764601/ http://www.ncbi.nlm.nih.gov/pubmed/37270750 http://dx.doi.org/10.1007/s10985-023-09601-y |
op_rights |
© The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
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https://doi.org/10.1007/s10985-023-09601-y |
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Lifetime Data Analysis |
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