Name
Abstract | ||
|---|---|---|
In many clinical trials, individuals in different subgroups may experience differential treatment effects. This leads to the need to consider individualized differences in treatment benefit. The general concept of predictive directions, which are risk scores motivated by potential outcomes considerations, is introduced. These techniques borrow heavily from the literature from sufficient dimension reduction (SDR) and causal inference. Initially directions assuming an idealized complete data structure are formulated. Then a new connection between SDR and kernel machine methodology for detection of treatmentcovariate interactions is developed. Simulation studies and a real data analysis from AIDS Clinical Trials Group (ACTG) 175 data show the utility of the proposed approach.(c) 2022 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.csda.2022.107476 | COMPUTATIONAL STATISTICS & DATA ANALYSIS |
Keywords | DocType | Volume |
Causal effect, Heterogeneity of treatment effect, Machine learning, Kernel methods, Personalized medicine | Journal | 174 |
ISSN | Citations | PageRank |
0167-9473 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youngjoo Cho | 1 | 0 | 0.34 |
| Xiang Zhan | 2 | 0 | 0.34 |
| Debashis Ghosh | 3 | 496 | 49.16 |