Competing Risks: Impact on Risk Estimation and Algorithmic Fairness

• URL: http://arxiv.org/abs/2508.05435v1
• Date: Thu, 07 Aug 2025 14:25:43 GMT
• Title: Competing Risks: Impact on Risk Estimation and Algorithmic Fairness
• Authors: Vincent Jeanselme, Brian Tom, Jessica Barrett,
• Abstract summary: Survival analysis accounts for patients who do not experience the event of interest during the study period, known as censored patients.<n> competing risks are often treated as censoring, a practice frequently overlooked due to a limited understanding of its consequences.<n>Our work shows why treating competing risks as censoring introduces substantial bias in survival estimates, leading to systematic overestimation of risk and, critically, amplifying disparities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate time-to-event prediction is integral to decision-making, informing medical guidelines, hiring decisions, and resource allocation. Survival analysis, the quantitative framework used to model time-to-event data, accounts for patients who do not experience the event of interest during the study period, known as censored patients. However, many patients experience events that prevent the observation of the outcome of interest. These competing risks are often treated as censoring, a practice frequently overlooked due to a limited understanding of its consequences. Our work theoretically demonstrates why treating competing risks as censoring introduces substantial bias in survival estimates, leading to systematic overestimation of risk and, critically, amplifying disparities. First, we formalize the problem of misclassifying competing risks as censoring and quantify the resulting error in survival estimates. Specifically, we develop a framework to estimate this error and demonstrate the associated implications for predictive performance and algorithmic fairness. Furthermore, we examine how differing risk profiles across demographic groups lead to group-specific errors, potentially exacerbating existing disparities. Our findings, supported by an empirical analysis of cardiovascular management, demonstrate that ignoring competing risks disproportionately impacts the individuals most at risk of these events, potentially accentuating inequity. By quantifying the error and highlighting the fairness implications of the common practice of considering competing risks as censoring, our work provides a critical insight into the development of survival models: practitioners must account for competing risks to improve accuracy, reduce disparities in risk assessment, and better inform downstream decisions.

Related papers

• HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks [51.95824566163554]
  We introduce HACSurv, a survival analysis method that learns Hierarchical Archimedean Copulas structures.<n>By capturing the dependencies between risks and censoring, HACSurv improves the accuracy of survival predictions.
  arXiv  Detail & Related papers  (2024-10-19T18:52:18Z)
• Auditing Fairness under Unobserved Confounding [56.61738581796362]
  We show that, surprisingly, one can still compute meaningful bounds on treatment rates for high-risk individuals.<n>We use the fact that in many real-world settings we have data from prior to any allocation to derive unbiased estimates of risk.
  arXiv  Detail & Related papers  (2024-03-18T21:09:06Z)
• Interpretable Survival Analysis for Heart Failure Risk Prediction [50.64739292687567]
  We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.
  arXiv  Detail & Related papers  (2023-10-24T02:56:05Z)
• Neural Fine-Gray: Monotonic neural networks for competing risks [0.0]
  Time-to-event modelling, known as survival analysis, differs from standard regression as it addresses censoring in patients who do not experience the event of interest. This paper leverages constrained monotonic neural networks to model each competing survival distribution. The effectiveness of the solution is demonstrated on one synthetic and three medical datasets.
  arXiv  Detail & Related papers  (2023-05-11T10:27:59Z)
• On (assessing) the fairness of risk score models [2.0646127669654826]
  Risk models are of interest for a number of reasons, including the fact that they communicate uncertainty about the potential outcomes to users. We identify the provision of similar value to different groups as a key desideratum for risk score fairness. We introduce a novel calibration error metric that is less sample size-biased than previously proposed metrics.
  arXiv  Detail & Related papers  (2023-02-17T12:45:51Z)
• A Survey of Risk-Aware Multi-Armed Bandits [84.67376599822569]
  We review various risk measures of interest, and comment on their properties. We consider algorithms for the regret minimization setting, where the exploration-exploitation trade-off manifests. We conclude by commenting on persisting challenges and fertile areas for future research.
  arXiv  Detail & Related papers  (2022-05-12T02:20:34Z)
• Two steps to risk sensitivity [4.974890682815778]
  conditional value-at-risk (CVaR) is a risk measure for modeling human and animal planning. We adopt a conventional distributional approach to CVaR in a sequential setting and reanalyze the choices of human decision-makers. We then consider a further critical property of risk sensitivity, namely time consistency, showing alternatives to this form of CVaR.
  arXiv  Detail & Related papers  (2021-11-12T16:27:47Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
  Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
  arXiv  Detail & Related papers  (2020-06-14T01:15:00Z)
• Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks [14.928328404160299]
  We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data. Our approach does not require making strong assumptions of constant proportional hazard of the underlying survival distribution. This is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.
  arXiv  Detail & Related papers  (2020-03-02T20:21:59Z)

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.