G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study.

Scientific Reports
Arthur ChattonYohann Foucher

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...Continue Reading

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Citations

Mar 17, 2021·European Journal of Cardio-thoracic Surgery : Official Journal of the European Association for Cardio-thoracic Surgery·Basile MarieThomas Sénage
Dec 5, 2021·Statistical Methods in Medical Research·Arthur ChattonYohann Foucher
Jan 8, 2022·Biometrical Journal. Biometrische Zeitschrift·Maxime LégerYohann Foucher

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Software Mentioned

RISCA
IPTW
LASSO
FM
computation
GC
TMLE
R
R package sandwich

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