Deep Optimal Individualized Treatment Rules for Bivariate Survival Outcomes via Adaptive Prediction-Powered Learning

• URL: http://arxiv.org/abs/2605.29464v1
• Date: Thu, 28 May 2026 06:56:52 GMT
• Title: Deep Optimal Individualized Treatment Rules for Bivariate Survival Outcomes via Adaptive Prediction-Powered Learning
• Abstract summary: This paper addresses the problem of deriving optimal individualized treatment rules to maximize the joint survival probability beyond fixed time points.<n>We propose a novel approach that models treatment rules via policies, coupling marginal accelerated failure time models via link function.<n>To enhance robustness and effectiveness of decision making, we introduce an adaptive prediction-powered method that leverages auxiliary predictions from machine learning models.
• Score: 3.890091933986142
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In randomized trials involving multiple treatments, bivariate survival outcomes present significant analytical challenges for making decisions. This paper addresses the problem of deriving optimal individualized treatment rules to maximize the joint survival probability beyond fixed time points $(t_1, t_2)$ through deep neural networks, while accounting for right censoring. We propose a novel approach that models treatment rules via stochastic policies, coupling marginal accelerated failure time models via link function to capture bivariate dependence. To enhance robustness and effectiveness of decision making, we introduce an adaptive prediction-powered method that leverages auxiliary predictions from machine learning models.

Related papers

• Adaptive Double-Booking Strategy for Outpatient Scheduling Using Multi-Objective Reinforcement Learning [0.3437656066916039]
  We propose an adaptive outpatient double-booking framework that integrates individualized no-show prediction with reinforcement learning.<n>The proposed framework determines when to single-book, double-book, or reject appointment requests.
  arXiv  Detail & Related papers  (2026-03-07T15:58:38Z)
• On the Optimality of Tracking Fisher Information in Adaptive Testing with Stochastic Binary Responses [3.491999371287298]
  We study the problem of estimating a continuous ability parameter from sequential binary responses.<n>We propose a simple algorithm that adaptively selects questions to maximize Fisher information.<n>We prove that this Fisher-tracking strategy achieves optimal performance in both fixed-confidence and fixed-budget regimes.
  arXiv  Detail & Related papers  (2025-10-09T07:10:00Z)
• An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes [55.93922317950527]
  We develop a novel meta-learner called DRQ-learner.<n>Our DRQ-learner is applicable to settings with both discrete and continuous state spaces.
  arXiv  Detail & Related papers  (2025-09-30T15:49:29Z)
• LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization [48.91511514636768]
  Length-Adaptive Policy Optimization transforms reasoning length control from an external constraint into an intrinsic model capability.<n>LAPO enables models to internalize an understanding of appropriate reasoning depth through a two-stage reinforcement learning process.<n> Experiments on mathematical reasoning benchmarks demonstrate that LAPO reduces token usage by up to 40.9% while improving accuracy by 2.3%.
  arXiv  Detail & Related papers  (2025-07-21T16:14:41Z)
• Robust Spatiotemporal Epidemic Modeling with Integrated Adaptive Outlier Detection [7.5504472850103435]
  In epidemic modeling, outliers can distort parameter estimation and lead to misguided public health decisions.<n>We introduce a robust generalized additive model (RST-GAM) to mitigate this distortion.<n>We demonstrate the practical utility of RST-GAM by analyzing county-level COVID-19 infection data in the United States.
  arXiv  Detail & Related papers  (2025-07-12T19:23:25Z)
• Likelihood Ratio Confidence Sets for Sequential Decision Making [51.66638486226482]
  We revisit the likelihood-based inference principle and propose to use likelihood ratios to construct valid confidence sequences. Our method is especially suitable for problems with well-specified likelihoods. We show how to provably choose the best sequence of estimators and shed light on connections to online convex optimization.
  arXiv  Detail & Related papers  (2023-11-08T00:10:21Z)
• Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference [4.7464518249313805]
  We develop a data-driven approach to represent parameter uncertainty accurately and tractably in a novel optimization problem formulation. Our experiments indicate that accounting for uncertainty in key epidemiological parameters can improve the efficacy of time-critical allocation decisions by 4-8%.
  arXiv  Detail & Related papers  (2023-06-30T03:49:52Z)
• TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective [50.675845725806724]
  We propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt) TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions. The proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-12-17T15:01:05Z)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• Post-hoc loss-calibration for Bayesian neural networks [25.05373000435213]
  We develop methods for correcting approximate posterior predictive distributions encouraging them to prefer high-utility decisions. In contrast to previous work, our approach is agnostic to the choice of the approximate inference algorithm.
  arXiv  Detail & Related papers  (2021-06-13T13:53:27Z)
• Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning [52.74071439183113]
  We study the predict-then-optimize framework in the context of sequential decision problems (formulated as MDPs) solved via reinforcement learning. Two significant computational challenges arise in applying decision-focused learning to MDPs.
  arXiv  Detail & Related papers  (2021-06-06T23:53:31Z)
• DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret [59.81290762273153]
  Dynamic treatment regimes (DTRs) are personalized, adaptive, multi-stage treatment plans that adapt treatment decisions to an individual's initial features and to intermediate outcomes and features at each subsequent stage. We propose a novel algorithm that, by carefully balancing exploration and exploitation, is guaranteed to achieve rate-optimal regret when the transition and reward models are linear.
  arXiv  Detail & Related papers  (2020-05-06T13:03:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.