Inverse-Weighted Survival Games
Xintian Han, Mark Goldstein, Aahlad Puli, Thomas Wies, Adler J, Perotte, Rajesh Ranganath

TL;DR
This paper introduces Inverse-Weighted Survival Games, a novel training framework that directly optimizes survival analysis criteria like Brier score by iteratively estimating and fixing the other distribution, leading to improved model evaluation.
Contribution
It proposes a new game-theoretic approach for survival analysis that jointly estimates failure and censoring distributions without prior knowledge, ensuring convergence to true distributions.
Findings
Optimizes Brier score on simulations
Applies method to cancer and patient data
Ensures stationary points at true distributions
Abstract
Deep models trained through maximum likelihood have achieved state-of-the-art results for survival analysis. Despite this training scheme, practitioners evaluate models under other criteria, such as binary classification losses at a chosen set of time horizons, e.g. Brier score (BS) and Bernoulli log likelihood (BLL). Models trained with maximum likelihood may have poor BS or BLL since maximum likelihood does not directly optimize these criteria. Directly optimizing criteria like BS requires inverse-weighting by the censoring distribution. However, estimating the censoring model under these metrics requires inverse-weighting by the failure distribution. The objective for each model requires the other, but neither are known. To resolve this dilemma, we introduce Inverse-Weighted Survival Games. In these games, objectives for each model are built from re-weighted estimates featuring the…
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Taxonomy
TopicsStatistical Methods and Inference · Machine Learning in Healthcare · Advanced Causal Inference Techniques
