ArticleStatistics in medicine2016
A flexible, interpretable framework for assessing sensitivity to unmeasured confounding.
Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantitative bias analysis in practice: review of software for regression with unmeasured confounding.BMC medical research methodology · 2023Pooled it
- Improving Sensitivity Analysis By Synthesizing Randomized Clinical Trials With Limited Overlap.Statistics in medicine · 2026Article
- Article
- Marginal causal effect of fungicide seed treatments on soybean yield and uncertain profitability in the US Midwest.Scientific reports · 2026Article
- Bayesian Sensitivity Analysis for Causal Estimation With Time-Varying Unmeasured Confounding.Statistics in medicine · 2026Article
- Assessing Unmeasured Confounding in Observational Research via Bayesian Semiparametric Modeling.Observational studies · 2026Article
- Cornfield's Pragmatic Contribution to Policy Discourse: Continued.Observational studies · 2026Article
- Getting to Know Worst-Case Confounders.Observational studies · 2026Article
- Flexible quantitative bias analysis for unmeasured confounding in subject-level indirect treatment comparisons with proportional hazards violation.BMC medical research methodology · 2025Article
- Matching-Assisted Power Prior for Incorporating Real-World Data in Randomized Clinical Trial Analysis.Statistics in medicine · 2025Article
- Bayesian doubly robust estimation of causal effects for clustered observational data.Journal of applied statistics · 2025Article
- unmconf : an R package for Bayesian regression with unmeasured confounders.BMC medical research methodology · 2024Article
- Sensitivity analysis of unmeasured confounding in causal inference based on exponential tilting and super learner.Journal of applied statistics · 2023Article
- Stan and BART for Causal Inference: Estimating Heterogeneous Treatment Effects Using the Power of Stan and the Flexibility of Machine Learning.Entropy (Basel, Switzerland) · 2022Article
- A Semiparametric Approach to Model-Based Sensitivity Analysis in Observational Studies.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences.Science advances · 2022Review
- Article
- Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models.Psychometrika · 2022Article
- Synthetic Negative Controls: Using Simulation to Screen Large-scale Propensity Score Analyses.Epidemiology (Cambridge, Mass.) · 2022Article
- A FLEXIBLE SENSITIVITY ANALYSIS APPROACH FOR UNMEASURED CONFOUNDING WITH MULTIPLE TREATMENTS AND A BINARY OUTCOME WITH APPLICATION TO SEER-MEDICARE LUNG CANCER DATA.The annals of applied statistics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis strategy that assesses sensitivity of posterior distributions of treatment effects to choices of sensitivity parameters. This results in an easily interpretable framework for testing for the impact of an unmeasured confounder that also limits the number of modeling assumptions. We evaluate our approach in a large-scale simulation setting and with high blood pressure data taken from the Third National Health and Nutrition Examination Survey. The model is implemented as open-source software, integrated into the treatSens package for the R statistical programming language. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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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.