Trial reportBMC medical research methodology2023
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.
Trial report in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Applying the Target Study Conceptual Model to Measure Racial and Ethnic Disparities in Hypertension Treatment Intensification.Epidemiology (Cambridge, Mass.) · 2026Article
- Article
- Diabetes is causally associated with increased breast cancer mortality by inducing FIBCD1 to activate MCM5-mediated cell cycle arrest via modulating H3K27ac.Cell death & disease · 2025Article
- Article
- The Target Study: A Conceptual Model and Framework for Measuring Disparity.Sociological methods & research · 2025Article
- Estimating treatment effects using parametric models as counter-factual evidence.BMC medical research methodology · 2025Article
- Causal Multistate Models to Evaluate Treatment Delay.Statistics in medicine · 2025Article
- Outcomes after intranasal human milk therapy in preterm infants with intraventricular hemorrhage.Journal of perinatology : official journal of the California Perinatal Association · 2025Article
- How to select predictive models for decision-making or causal inference.GigaScience · 2025Article
- Estimating the causal effect of dexamethasone versus hydrocortisone on the neutrophil- lymphocyte ratio in critically ill COVID-19 patients from Tygerberg Hospital ICU using TMLE method.BMC infectious diseases · 2024Article
- Evaluating Effects of Multilevel Interventions on Disparity in Health and Healthcare Decisions.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Efficacy of Definitive Radiotherapy for Patients with Clinical Stage IIIB or IIIC Lung Adenocarcinoma and Epidermal Growth Factor Receptor (EGFR) Mutations Treated Using First- or Second-Generation EGFR Tyrosine Kinase Inhibitors.Canadian respiratory journal · 2024Article
- Implementing TMLE in the presence of a continuous outcome.Research methods in medicine & health sciences · 2024Article
- How to perform prespecified subgroup analyses when using propensity score methods in the case of imbalanced subgroups.BMC medical research methodology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo have confidence in one's interpretation of treatment effects assessed by comparing trial results to external controls, minimizing bias is a critical step. We sought to investigate different methods for causal inference in simulated data sets with measured and unmeasured confounders.
methodsThe simulated data included three types of outcomes (continuous, binary, and time-to-event), treatment assignment, two measured baseline confounders, and one unmeasured confounding factor. Three scenarios were set to create different intensities of confounding effect (e.g., small and blocked confounding paths, medium and blocked confounding paths, and one large unblocked confounding path for scenario 1 to 3, respectively) caused by the unmeasured confounder. The methods of g-computation (GC), inverse probability of treatment weighting (IPTW), overlap weighting (OW), standardized mortality/morbidity ratio (SMR), and targeted maximum likelihood estimation (TMLE) were used to estimate average treatment effects and reduce potential biases.
resultsThe results with the greatest extent of biases were from the raw model that ignored all the potential confounders. In scenario 2, the unmeasured factor indirectly influenced the treatment assignment through a measured controlling factor and led to medium confounding. The methods of GC, IPTW, OW, SMR, and TMLE removed most of bias observed in average treatment effects for all three types of outcomes from the raw model. Similar results were found in scenario 1, but the results tended to be biased in scenario 3. GC had the best performance followed by OW.
conclusionsThe aforesaid methods can be used for causal inference in externally controlled studies when there is no large, unblockable confounding path for an unmeasured confounder. GC and OW are the preferable approaches.
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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 Socratic graph.