Estimating heterogeneous treatment effects for latent subgroups in observational studies

DOI10.1002/sim.7970zbMATH Open1545.62386MaRDI QIDQ6625560

Edward J. Nehus, Mi-Ok Kim, Hang Joon Kim, Bo Lu

Publication date: 28 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

subgroup analysispropensity score matchingBayesian latent modelpediatric kidney transplant

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• BART: Bayesian additive regression trees
• Regularized outcome weighted subgroup identification for differential treatment effects
• Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies
• The central role of the propensity score in observational studies for causal effects
• A Multivariate Test of Interaction for Use in Clinical Trials
• Latent Subgroup Analysis of a Randomized Clinical Trial through a Semiparametric Accelerated Failure Time Mixture Model
• A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
• Design of observational studies
• Estimating optimal treatment regimes from a classification perspective
• Interaction trees with censored survival data

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Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data

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