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stat.ME2025
Quantifying uncertainty of individualized treatment effects in right-censored survival data: A comparison of Bayesian additive regression trees and causal survival forest
Daijiro Kabata, Nicholas C. Henderson, Ravi Varadhan
Estimation of individualized treatment effects (ITE), also known as conditional average treatment effects (CATE), is an active area of methodology development. However, much less a…
stat.ME2021
Nonparametric Analysis of Delayed Treatment Effects using Single-Crossing Constraints
Nicholas C. Henderson, Kijoeng Nam, Dai Feng
Clinical trials involving novel immuno-oncology (IO) therapies frequently exhibit survival profiles which violate the proportional hazards assumption due to a delay in treatment ef…
stat.ME2017
Individualized Treatment Effects with Censored Data via Fully Nonparametric Bayesian Accelerated Failure Time Models
Nicholas C. Henderson, Thomas A. Louis, Gary L. Rosner +1
Individuals often respond differently to identical treatments, and characterizing such variability in treatment response is an important aim in the practice of personalized medicin…