Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time‐To‐Event Outcomes
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| Format: | Artículo Open Access |
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Wiley
2025
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| author | Denis Talbot Awa Diop Miceline Mésidor Yohann Chiu Caroline Sirois Andrew J. Spieker Antoine Pariente Pernelle Noize Marc Simard Miguel Angel Luque Fernandez Michael Schomaker Kenji Fujita Danijela Gnjidic Mireille E. Schnitzer |
| author_facet | Denis Talbot Awa Diop Miceline Mésidor Yohann Chiu Caroline Sirois Andrew J. Spieker Antoine Pariente Pernelle Noize Marc Simard Miguel Angel Luque Fernandez Michael Schomaker Kenji Fujita Danijela Gnjidic Mireille E. Schnitzer Denis Talbot Awa Diop Miceline Mésidor Yohann Chiu Caroline Sirois Andrew J. Spieker Antoine Pariente Pernelle Noize Marc Simard Miguel Angel Luque Fernandez Michael Schomaker Kenji Fujita Danijela Gnjidic Mireille E. Schnitzer |
| collection | Wiley Open Access |
| contents | Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time‐To‐Event Outcomes Denis Talbot Awa Diop Miceline Mésidor Yohann Chiu Caroline Sirois Andrew J. Spieker Antoine Pariente Pernelle Noize Marc Simard Miguel Angel Luque Fernandez Michael Schomaker Kenji Fujita Danijela Gnjidic Mireille E. Schnitzer Statistics in Medicine ABSTRACTTargeted maximum likelihood estimation (TMLE) is an increasingly popular framework for the estimation of causal effects. It requires modeling both the exposure and outcome but is doubly robust in the sense that it is valid if at least one of these models is correctly specified. In addition, TMLE allows for flexible modeling of both the exposure and outcome with machine learning methods. This provides better control for measured confounders since the model specification automatically adapts to the data, instead of needing to be specified by the analyst a priori. Despite these methodological advantages, TMLE remains less popular than alternatives in part because of its less accessible theory and implementation. While some tutorials have been proposed, none address the case of a time‐to‐event outcome. This tutorial provides a detailed step‐by‐step explanation of the implementation of TMLE for estimating the effect of a point binary or multilevel exposure on a time‐to‐event outcome, modeled as counterfactual survival curves and causal hazard ratios. The tutorial also provides guidelines on how best to use TMLE in practice, including aspects related to study design, choice of covariates, controlling biases and use of machine learning. R‐code is provided to illustrate each step using simulated data ( https://github.com/detal9/SurvTMLE). To facilitate implementation, a general R function implementing TMLE with options to use machine learning is also provided. The method is illustrated in a real‐data analysis concerning the effectiveness of statins for the prevention of a first cardiovascular disease among older adults in Québec, Canada, between 2013 and 2018. 10.1002/sim.70034 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/sim.70034 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70034 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time‐To‐Event Outcomes Denis Talbot Awa Diop Miceline Mésidor Yohann Chiu Caroline Sirois Andrew J. Spieker Antoine Pariente Pernelle Noize Marc Simard Miguel Angel Luque Fernandez Michael Schomaker Kenji Fujita Danijela Gnjidic Mireille E. Schnitzer Statistics in Medicine Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time‐To‐Event Outcomes Denis Talbot Awa Diop Miceline Mésidor Yohann Chiu Caroline Sirois Andrew J. Spieker Antoine Pariente Pernelle Noize Marc Simard Miguel Angel Luque Fernandez Michael Schomaker Kenji Fujita Danijela Gnjidic Mireille E. Schnitzer Statistics in Medicine ABSTRACTTargeted maximum likelihood estimation (TMLE) is an increasingly popular framework for the estimation of causal effects. It requires modeling both the exposure and outcome but is doubly robust in the sense that it is valid if at least one of these models is correctly specified. In addition, TMLE allows for flexible modeling of both the exposure and outcome with machine learning methods. This provides better control for measured confounders since the model specification automatically adapts to the data, instead of needing to be specified by the analyst a priori. Despite these methodological advantages, TMLE remains less popular than alternatives in part because of its less accessible theory and implementation. While some tutorials have been proposed, none address the case of a time‐to‐event outcome. This tutorial provides a detailed step‐by‐step explanation of the implementation of TMLE for estimating the effect of a point binary or multilevel exposure on a time‐to‐event outcome, modeled as counterfactual survival curves and causal hazard ratios. The tutorial also provides guidelines on how best to use TMLE in practice, including aspects related to study design, choice of covariates, controlling biases and use of machine learning. R‐code is provided to illustrate each step using simulated data ( https://github.com/detal9/SurvTMLE). To facilitate implementation, a general R function implementing TMLE with options to use machine learning is also provided. The method is illustrated in a real‐data analysis concerning the effectiveness of statins for the prevention of a first cardiovascular disease among older adults in Québec, Canada, between 2013 and 2018. 10.1002/sim.70034 http://creativecommons.org/licenses/by/4.0/ |
| title | Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time‐To‐Event Outcomes |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70034 |