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Online Survival Analysis: A Bandit Approach under Cox PH Model

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel bandit approach for online survival analysis, integrating the Cox proportional hazards model into sequential decision-making. This method addresses challenges like staggered entry, delayed feedback, and right censoring, adapting existing bandit algorithms to optimize treatments as new data emerges. Simulations and experiments with SEER cancer data indicate the approach effectively learns near-optimal treatment policies. AI

IMPACT Introduces a new framework for sequential decision-making in survival analysis, potentially improving treatment optimization in medical settings.

RANK_REASON This is a research paper introducing a new methodology for survival analysis in an online learning setting.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Online Survival Analysis: A Bandit Approach under Cox PH Model

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This is a research paper introducing a new methodology for survival analysis in an online learning setting.
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Rui Song ·

    Online Survival Analysis: A Bandit Approach under Cox PH Model

    Survival analysis is a widely used statistical framework for modeling time-to-event data under censoring. Classical methods, such as the Cox proportional hazards (Cox PH) model, offer a semiparametric approach to estimating the effects of covariates on the hazard function. Despit…

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