Name
Abstract | ||
|---|---|---|
We develop some statistical methods based on tree-structured classification to assign patients from different treatment groups into subgroups with different medical recommendations. Since it is difficult to discover treatments that benefit all patients, we want to identify subgroups of patients for whom the treatment has an enhanced effect. We classify each terminal node into a subgroup by comparing the relative event rates of the node and its immediate predecessor. Given the suggested subgroup of each terminal node, we propose a method on how to identify which of the splitting variables are keen to which subgroup by tracing back along the tree. We also propose two different ways of assigning medical recommendations. Performance of the proposed method is evaluated using bagging multiple trees, and using cross validations on single trees as our benchmark model. Evaluation of the results is based on comparing the out of bag accuracy from bagging trees and the test accuracy from the cross-validated trees. Bagging trees with randomly selected features perform better than bagging trees including all features when the number of bagging trees exceeds the necessary ntree number. The bagging trees outperform the benchmark, especially when the treatment effect is more homogeneous. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1080/03610918.2019.1636998 | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION |
Keywords | DocType | Volume |
Bagging, Censoring, Proportional hazards regression, Subgroup analysis, Survival analysis | Journal | 50 |
Issue | ISSN | Citations |
12 | 0361-0918 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qing Zhang | 1 | 0 | 1.35 |
| Hongshik Ahn | 2 | 73 | 4.45 |