Random Forests of Interaction Trees for Estimating Individualized Treatment Effects in Randomized Trials
This work addresses the need for precision medicine by improving ITE estimation, but it is incremental as it builds on existing interaction trees with a speed-up modification.
The authors tackled the problem of estimating individualized treatment effects (ITE) in randomized trials by proposing a method called random forests of interaction trees (RFIT), which outperforms the traditional separate regression approach in simulation and an acupuncture headache trial analysis.
Assessing heterogeneous treatment effects has become a growing interest in advancing precision medicine. Individualized treatment effects (ITE) play a critical role in such an endeavor. Concerning experimental data collected from randomized trials, we put forward a method, termed random forests of interaction trees (RFIT), for estimating ITE on the basis of interaction trees (Su et al., 2009). To this end, we first propose a smooth sigmoid surrogate (SSS) method, as an alternative to greedy search, to speed up tree construction. RFIT outperforms the traditional `separate regression' approach in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT can be obtained with the infinitesimal jackknife method. We assess and illustrate the use of RFIT via both simulation and the analysis of data from an acupuncture headache trial.