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Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome
Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variab...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392888/ https://www.ncbi.nlm.nih.gov/pubmed/37462467 http://dx.doi.org/10.1097/EDE.0000000000001630 |
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author | Denz, Robin Timmesfeld, Nina |
author_facet | Denz, Robin Timmesfeld, Nina |
author_sort | Denz, Robin |
collection | PubMed |
description | Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variable of interest is continuous. Simple workarounds, such as categorizing the continuous covariate and plotting survival curves for each category, can result in misleading depictions of the main effects. Instead, we propose a new graphic, the survival area plot, to directly depict the survival probability over time and as a function of a continuous covariate simultaneously. This plot utilizes g-computation based on a suitable time-to-event model to obtain the relevant estimates. Through the use of g-computation, those estimates can be adjusted for confounding without additional effort, allowing a causal interpretation under the standard causal identifiability assumptions. If those assumptions are not met, the proposed plot may still be used to depict noncausal associations. We illustrate and compare the proposed graphics to simpler alternatives using data from a large German observational study investigating the effect of the Ankle-Brachial Index on survival. To facilitate the usage of these plots, we additionally developed the contsurvplot R-package, which includes all methods discussed in this paper. |
format | Online Article Text |
id | pubmed-10392888 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-103928882023-08-02 Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome Denz, Robin Timmesfeld, Nina Epidemiology Methods Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variable of interest is continuous. Simple workarounds, such as categorizing the continuous covariate and plotting survival curves for each category, can result in misleading depictions of the main effects. Instead, we propose a new graphic, the survival area plot, to directly depict the survival probability over time and as a function of a continuous covariate simultaneously. This plot utilizes g-computation based on a suitable time-to-event model to obtain the relevant estimates. Through the use of g-computation, those estimates can be adjusted for confounding without additional effort, allowing a causal interpretation under the standard causal identifiability assumptions. If those assumptions are not met, the proposed plot may still be used to depict noncausal associations. We illustrate and compare the proposed graphics to simpler alternatives using data from a large German observational study investigating the effect of the Ankle-Brachial Index on survival. To facilitate the usage of these plots, we additionally developed the contsurvplot R-package, which includes all methods discussed in this paper. Lippincott Williams & Wilkins 2023-06-29 2023-09 /pmc/articles/PMC10392888/ /pubmed/37462467 http://dx.doi.org/10.1097/EDE.0000000000001630 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Methods Denz, Robin Timmesfeld, Nina Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome |
title | Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome |
title_full | Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome |
title_fullStr | Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome |
title_full_unstemmed | Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome |
title_short | Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome |
title_sort | visualizing the (causal) effect of a continuous variable on a time-to-event outcome |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392888/ https://www.ncbi.nlm.nih.gov/pubmed/37462467 http://dx.doi.org/10.1097/EDE.0000000000001630 |
work_keys_str_mv | AT denzrobin visualizingthecausaleffectofacontinuousvariableonatimetoeventoutcome AT timmesfeldnina visualizingthecausaleffectofacontinuousvariableonatimetoeventoutcome |