Model-Induced Hazard Geometry and Functional Representations in Survival Analysis
Abstract
Individualized survival predictions are often summarized by scalar risk scores or event probabilities at selected time horizons, although clinically relevant risk may also differ in its temporal organization. We consider finite-dimensional representations of individual hazard trajectories through interpretable hazard functionals, such as peak timing, cumulative hazard over prespecified intervals, and persistence of elevated risk. A central issue is that the richness of such representations is constrained by the hazard geometry supported by the underlying survival model. We formalize this through model-induced tangent spaces and functional ranks, derive an exact decomposition of model-supported variation into directions retained and lost by a chosen representation, and introduce a model-relative notion of local completeness. We further distinguish intrinsic model capacity from the variation actually activated by covariates. These results motivate a structured extension of AFT time scaling through a positive-affine deformation on the log-time scale, yielding a power-warp mechanism with an additional temporal deformation direction. Combined with a log-logistic reference hazard, this gives a parsimonious likelihood-based working specification for which peak timing and peak height provide complete coordinates over the unimodal regime. We also establish plug-in recovery and asymptotic inference for hazard functionals. Simulations and a lung-cancer application illustrate how model-induced restrictions propagate to hazard-functional representations.
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BibTeX
@misc{liu2026modelinduced,
title = {{Model-Induced Hazard Geometry and Functional Representations in Survival Analysis}},
author = {Xijia Liu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.19643},
eprint = {2610.19643},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.19643},
}
RIS
TY - UNPB
TI - Model-Induced Hazard Geometry and Functional Representations in Survival Analysis
AU - Xijia Liu
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2610.19643
UR - https://arxiv.org/abs/2610.19643
AB - Individualized survival predictions are often summarized by scalar risk scores or event probabilities at selected time horizons, although clinically relevant risk may also differ in its temporal organization. We consider finite-dimensional representations of individual hazard trajectories through interpretable hazard functionals, such as peak timing, cumulative hazard over prespecified intervals, and persistence of elevated risk. A central issue is that the richness of such representations is constrained by the hazard geometry supported by the underlying survival model. We formalize this through model-induced tangent spaces and functional ranks, derive an exact decomposition of model-supported variation into directions retained and lost by a chosen representation, and introduce a model-relative notion of local completeness. We further distinguish intrinsic model capacity from the variation actually activated by covariates. These results motivate a structured extension of AFT time scaling through a positive-affine deformation on the log-time scale, yielding a power-warp mechanism with an additional temporal deformation direction. Combined with a log-logistic reference hazard, this gives a parsimonious likelihood-based working specification for which peak timing and peak height provide complete coordinates over the unimodal regime. We also establish plug-in recovery and asymptotic inference for hazard functionals. Simulations and a lung-cancer application illustrate how model-induced restrictions propagate to hazard-functional representations.
ER -