G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study
Abstract Controlling for confounding bias is crucial in causal inference. Distinct methods are currently employed to mitigate the effects of confounding bias. Each requires the introduction of a set of covariates, which remains difficult to choose, especially regarding the different methods. We cond...
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2020
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oai:doaj.org-article:694ea9cc77664ee690089eec2f9159a92021-12-02T17:52:42ZG-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study10.1038/s41598-020-65917-x2045-2322https://doaj.org/article/694ea9cc77664ee690089eec2f9159a92020-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-65917-xhttps://doaj.org/toc/2045-2322Abstract Controlling for confounding bias is crucial in causal inference. Distinct methods are currently employed to mitigate the effects of confounding bias. Each requires the introduction of a set of covariates, which remains difficult to choose, especially regarding the different methods. We conduct a simulation study to compare the relative performance results obtained by using four different sets of covariates (those causing the outcome, those causing the treatment allocation, those causing both the outcome and the treatment allocation, and all the covariates) and four methods: g-computation, inverse probability of treatment weighting, full matching and targeted maximum likelihood estimator. Our simulations are in the context of a binary treatment, a binary outcome and baseline confounders. The simulations suggest that considering all the covariates causing the outcome led to the lowest bias and variance, particularly for g-computation. The consideration of all the covariates did not decrease the bias but significantly reduced the power. We apply these methods to two real-world examples that have clinical relevance, thereby illustrating the real-world importance of using these methods. We propose an R package RISCA to encourage the use of g-computation in causal inference.Arthur ChattonFlorent Le BorgneClémence LeyratFlorence GillaizeauChloé RousseauLaetitia BarbinDavid LaplaudMaxime LégerBruno GiraudeauYohann FoucherNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020) |
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Medicine R Science Q Arthur Chatton Florent Le Borgne Clémence Leyrat Florence Gillaizeau Chloé Rousseau Laetitia Barbin David Laplaud Maxime Léger Bruno Giraudeau Yohann Foucher G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
| description |
Abstract Controlling for confounding bias is crucial in causal inference. Distinct methods are currently employed to mitigate the effects of confounding bias. Each requires the introduction of a set of covariates, which remains difficult to choose, especially regarding the different methods. We conduct a simulation study to compare the relative performance results obtained by using four different sets of covariates (those causing the outcome, those causing the treatment allocation, those causing both the outcome and the treatment allocation, and all the covariates) and four methods: g-computation, inverse probability of treatment weighting, full matching and targeted maximum likelihood estimator. Our simulations are in the context of a binary treatment, a binary outcome and baseline confounders. The simulations suggest that considering all the covariates causing the outcome led to the lowest bias and variance, particularly for g-computation. The consideration of all the covariates did not decrease the bias but significantly reduced the power. We apply these methods to two real-world examples that have clinical relevance, thereby illustrating the real-world importance of using these methods. We propose an R package RISCA to encourage the use of g-computation in causal inference. |
| format |
article |
| author |
Arthur Chatton Florent Le Borgne Clémence Leyrat Florence Gillaizeau Chloé Rousseau Laetitia Barbin David Laplaud Maxime Léger Bruno Giraudeau Yohann Foucher |
| author_facet |
Arthur Chatton Florent Le Borgne Clémence Leyrat Florence Gillaizeau Chloé Rousseau Laetitia Barbin David Laplaud Maxime Léger Bruno Giraudeau Yohann Foucher |
| author_sort |
Arthur Chatton |
| title |
G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
| title_short |
G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
| title_full |
G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
| title_fullStr |
G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
| title_full_unstemmed |
G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
| title_sort |
g-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
| publisher |
Nature Portfolio |
| publishDate |
2020 |
| url |
https://doaj.org/article/694ea9cc77664ee690089eec2f9159a9 |
| work_keys_str_mv |
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