Abstract
We propose a projection pursuit technique in survival analysis for finding lower-dimensional projections that exhibit differentiated survival outcomes. This idea is formally introduced as the change-plane Cox model, a nonregular Cox model with a change-plane in the covariate space that divides the population into two subgroups whose hazards are proportional. The proposed technique offers a potential framework for principled subgroup discovery. Estimation of the change-plane is accomplished via likelihood maximization over a data-driven sieve constructed using sliced inverse regression. Consistency of the sieve procedure for the change-plane parameters is established. In simulations the sieve estimator demonstrates better classification performance for subgroup identification than alternatives.
Original language | English |
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Pages (from-to) | 891-903 |
Number of pages | 13 |
Journal | Biometrika |
Volume | 105 |
Issue number | 4 |
DOIs | |
Publication status | Published - Dec 2018 |
Externally published | Yes |
Keywords
- Latent supervised learning
- Projection pursuit
- Random projection
- Sieve estimation
- Sliced inverse regression
- Subgroup discovery.