ArticleStatistical methods in medical research2020
Estimation of causal effects of multiple treatments in observational studies with a binary outcome.
Article in Statistical methods in medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
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- Impact of ceiling of care on mortality across four COVID-19 epidemic waves in Catalonia: a multicentre prospective cohort study.BMJ open · 2025Article
- COMPARATIVE EFFECTIVENESS OF PROPENSITY SCORE ESTIMATION METHODS FOR INVERSE PROBABILITY OF TREATMENT WEIGHTING ANALYSIS WITH COMPLEX SURVEY DATA: A SIMULATION STUDY.Journal of survey statistics and methodology · 2025Article
- A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding.Lifetime data analysis · 2025Article
- Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.BMC public health · 2025Article
- Interpretable AI for inference of causal molecular relationships from omics data.Science advances · 2025Article
- Developing a framework for identifying risk factors and estimating direct economic disease burden attributable to healthcare-associated infections: a case study of a Chinese Tuberculosis hospital.Global health research and policy · 2024Article
- Targeted learning in observational studies with multi-valued treatments: An evaluation of antipsychotic drug treatment safety.Statistics in medicine · 2024Article
- Heterogeneous treatment effect estimation for observational data using model-based forests.Statistical methods in medical research · 2024Article
- A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Estimating the causal effects of multiple intermittent treatments with application to COVID-19.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Comparison of robotic versus laparoscopic versus open distal gastrectomy for locally advanced gastric cancer: a prospective trial-based economic evaluation.Surgical endoscopy · 2023Article
- Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study.Frontiers in public health · 2023Article
- Complete mesocolic excision for right colonic cancer: prospective multicentre study.The British journal of surgery · 2022Article
- Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series.International journal of environmental research and public health · 2022Review
- A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.International journal of environmental research and public health · 2022Article
- A flexible approach for causal inference with multiple treatments and clustered survival outcomes.Statistics in medicine · 2022Article
- CIMTx: An R Package for Causal Inference with Multiple Treatments using Observational Data.The R journal · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting, targeted maximum likelihood estimator, vector matching, and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the treatment assignment and outcome generating mechanisms, Bayesian additive regression trees, targeted maximum likelihood estimator, and inverse probability of treatment weighting using generalized boosted models provide better bias reduction and smaller root mean squared error. Bayesian additive regression trees and targeted maximum likelihood estimator provide more consistent 95% confidence interval coverage and better large-sample convergence property. Second, we supply Bayesian additive regression trees with a strategy to identify a common support region for retaining inferential units and for avoiding extrapolating over areas of the covariate space where common support does not exist. Bayesian additive regression trees retain more inferential units than the generalized propensity score-based strategy, and shows lower bias, compared to targeted maximum likelihood estimator or generalized boosted model, in a variety of scenarios differing by the degree of covariate overlap. A case study examining the effects of three surgical approaches for non-small cell lung cancer demonstrates the methods.
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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 OpenQuestion graph.