ArticleScientific reports2020
G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
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- Target trial emulation to assess real-world efficacy in the Epidemiological Strategy and Medical Economics metastatic breast cancer cohort.Journal of the National Cancer Institute · 2023Trial
- Comparing g-computation, propensity score-based weighting, and targeted maximum likelihood estimation for analyzing externally controlled trials with both measured and unmeasured confounders: a simulation study.BMC medical research methodology · 2023Trial
- Outcomes of restorative proctectomy and ileal pouch-anal anastomosis: a matched-cohort analysis of robot-assisted, laparoscopic, and open surgery.Surgical endoscopy · 2026Article
- Loneliness and cognitive function in later life: a population-based counterfactual analysis.International journal of epidemiology · 2026Article
- Translating Observational Data Into Public Health Action: Reducing Early Childhood Caries Burden Related to Socioeconomic Disparities.Community dentistry and oral epidemiology · 2026Article
- High-flow nasal cannula oxygen during breaks from noninvasive ventilation after extubation: an observational study.Intensive care medicine · 2026Observational
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- Are Drains After Trauma Splenectomy Associated with Organ Space Surgical Site Infections?Surgical infections · 2026Article
- REFINE2: a simplified simulation tool to help epidemiologists evaluate the suitability and sensitivity of effect estimation within user-specified data.American journal of epidemiology · 2026Article
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- Investigating the causal effects of religiosity on childbearing among U.S. adolescents using a three-wave longitudinal design.Scientific reports · 2026Article
- Comparative clinical response, safety, and institutional drug use efficiency of intravenous azithromycin versus erythromycin in pediatricFrontiers in cellular and infection microbiology · 2026Article
- Universal same-day discharge after colonic resection: a matched cohort study.Updates in surgery · 2025Article
- Evaluation of Methods Adjusting for Unmeasured Confounding Using Large Healthcare Databases: An Empirical Study Concerning Drugs Inducing Prematurity.Clinical and translational science · 2025Article
- Article
- Accounting for Misclassification of Binary Outcomes in External Control Arm Studies for Unanchored Indirect Comparisons: Simulations and Applied Example.Statistics in medicine · 2025Article
- FedECA: federated external control arms for causal inference with time-to-event data in distributed settings.Nature communications · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.