竞争风险治愈模型：方法学文献的五轴系统综述

中文摘要 AI 辅助

竞争风险治愈模型用于描述时间-事件总体，其中包含对所有事件类型或目标事件免疫的个体，但现有文献在各模型族间存在碎片化问题。我们针对五个轴综述了26篇论文：治愈的定义/范围、分解/治愈机制、潜伏期、依赖关系、删失与隐藏原因，以及估计方法。我们区分了全局治愈与病因特异性治愈、发病率-潜伏期混合模型与垂直易感因子分解模型、潜在竞争原因/零计数构造、缺陷生存模型，以及零膨胀混合模型或累积发病率函数（CIF）形式。我们比较了参数型、分段常数型、PH、AFT、变换型、基于CIF的、非参数型和部分指定型潜伏期模型，适用于右删失、区间删失、聚类数据和隐藏原因。混合形式占据主导地位，但相似名称可能掩盖不同的估计目标、治愈机制、潜在风险/删失假设和回归解释。潜在失效依赖关系的建模频率低于治愈或潜伏期；失效-删失依赖关系、聚类内关联和隐藏原因仅在较小的子集中被考虑。估计方法涵盖似然法和期望最大化（EM）算法，包括神经网络M步、估计方程、逆概率删失加权、贝叶斯计算和copula-图形估计。对公开骨髓移植数据的可重复缺陷Gompertz分析表明，拟合的尾部概率需要针对特定模型和终点进行解释，而非所有方法的比较。可重复性仍然有限：大多数实现使用自定义代码，很少提供代码库访问权限，且没有广泛采用、许可清晰的R/Python框架来统一这些构造。本分类法支持透明的模型选择/报告、估计方法比较，以及理论、软件、基准测试和可重复应用的需求。

英文摘要

Competing-risk cure models describe time-to-event populations with individuals immune to all event types or an event of interest, yet literature is fragmented across model families. We review 26 papers across five axes: cure definition/scope; decomposition/cure mechanism; latency; dependence, censoring, and masked causes; and estimation. We distinguish global from cause-specific cure and incidence--latency mixtures from vertical susceptibility factorizations, latent competing-causes/zero-count constructions, defective-survival models, and zero-inflated mixture or cumulative incidence function (CIF) formulations. We compare parametric, piecewise-constant, PH, AFT, transformation, CIF-based, nonparametric, and partially specified latency models for right/interval censoring, clustering, and masked causes. Mixture formulations dominate, but similar names can mask different estimands, cure mechanisms, latent-risk/censoring assumptions, and regression interpretations. Latent-failure dependence is modeled less often than cure or latency; failure--censoring dependence, within-cluster association, and masked causes occur in smaller subsets. Estimation spans likelihood and expectation-maximization (EM), including neural-network M-steps, estimating equations, inverse-probability-of-censoring weighting, Bayesian computation, and copula-graphic estimation. A reproducible defective-Gompertz analysis of public bone-marrow-transplant data shows that fitted tail probabilities require model- and endpoint-specific interpretation, not all-method comparison. Reproducibility remains limited: most implementations use custom code, few offer repository access, and no widely adopted, clearly licensed R/Python framework unifies the constructions. This taxonomy supports transparent model selection/reporting, estimation-method comparison, and needs for theory, software, benchmarking, and reproducible applications.

发表机构

• University of Texas at El Paso（埃尔帕索德克萨斯大学）

机构由 AI 辅助整理，请以论文原文为准。

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34 pages, 2 figures