Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.
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2024-03
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Abstract
Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to better estimate heterogeneous treatment effects. This article discusses several nonparametric approaches for estimating heterogeneous treatment effects using data from multiple trials. We extend single-study methods to a scenario with multiple trials and explore their performance through a simulation study, with data generation scenarios that have differing levels of cross-trial heterogeneity. The simulations demonstrate that methods that directly allow for heterogeneity of the treatment effect across trials perform better than methods that do not, and that the choice of single-study method matters based on the functional form of the treatment effect. Finally, we discuss which methods perform well in each setting and then apply them to four randomized controlled trials to examine effect heterogeneity of treatments for major depressive disorder.
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Brantner, Carly Lupton, Trang Quynh Nguyen, Tengjie Tang, Congwen Zhao, Hwanhee Hong and Elizabeth A Stuart (2024). Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects. Statistics in medicine, 43(7). pp. 1291–1314. 10.1002/sim.9955 Retrieved from https://hdl.handle.net/10161/31324.
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Carly Brantner
Carly L. Brantner, PhD, joined the Department of Biostatistics and Bioinformatics and the Duke Clinical Research Institute in 2024. She is both a methodological and collaborative biostatistician. Her methodological background primarily centers around causal inference, focusing on developing and adapting machine learning methods to integrate multiple data sources and estimate heterogeneous treatment effects. She is particularly interested in aiding efficient and effective personalized treatment decisions through robust statistical approaches. She is passionate about impacting health across many areas, including but not limited to female health, mental health, sport science, musculoskeletal systems and function, and aging.

Hwanhee Hong
I am interested in developing Bayesian statistical methods for comparative effectiveness research, network meta-analysis, causal inference, measurement error, and generalizability. A flexible Bayesian modeling framework enables us to easily integrate different data sources and borrow information adaptively across them. The ultimate goal of my research is to provide comprehensive evidence from multiple data sources for answering clinical and scientific questions in public health and medicine.
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