针对随访时间不定的不良事件的生存分析（SAVVY）——不良事件风险的估计

Siamese Survival Analysis with Competing Risks

Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiability, or inability to estimate true cause-specific survival curves from empirical data, further complicates competing risk survival analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep learning architecture for estimating personalized risk scores in the p…

无需分布的低风险肿瘤患者生存时间 horizon 选择

Distribution-Free Selection of Low-Risk Oncology Patients for Survival Beyond a Time Horizon

本文提出两种无分布框架，用于在固定时间范围内选择低风险肿瘤患者，通过校准筛查规则实现生存时间的保证。

Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary

Many researchers have investigated first hitting times as models for survival data. First hitting times arise naturally in many types of stochastic processes, ranging from Wiener processes to Markov chains. In a survival context, the state of the underlying process represents the strength of an item or the health of an individual. The item fails or the individual experiences a clinical endpoint when the process reaches an adverse threshold state for the first time. The time scale can be calendar time or some other operational measure of degradation or disease progression. In many applications,…

预测心房颤动导管消融治疗后的不良结局

Predicting adverse outcomes following catheter ablation treatment for atrial fibrillation

本研究利用澳大利亚新南威尔士州3285名AF导管消融患者的关联数据，开发了传统和深度生存模型预测不良结局。结果显示复合结局预测模型表现出色（一致性指数>0.79），而主要出血事件预测效果不佳，有助于临床识别高危患者。

Federated Survival Analysis with Discrete-Time Cox Models

Building machine learning models from decentralized datasets located in different centers with federated learning (FL) is a promising approach to circumvent local data scarcity while preserving privacy. However, the prominent Cox proportional hazards (PH) model, used for survival analysis, does not fit the FL framework, as its loss function is non-separable with respect to the samples. The naïve method to bypass this non-separability consists in calculating the losses per center, and minimizing their sum as an approximation of the true loss. We show that the resulting model may suffer from imp…

A tutorial on evaluating time-varying discrimination accuracy for survival models used in dynamic decision-making

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic mode can identify patients at greatest risk for future adverse events, and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance, i.e. sensitivity and specificity, for distinguishing high-risk versus lo…

Individualized Dynamic Prediction of Survival under Time-Varying Treatment Strategies

Often in follow-up studies intermediate events occur in some patients, such as reinterventions or adverse events. These intermediate events directly affect the shapes of their longitudinal profiles. Our work is motivated by two studies in which such intermediate events have been recorded during follow-up. The first study concerns Congenital Heart Diseased patients who were followed-up echocardiographically, with several patients undergoing reintervention. The second study concerns patients who participated in the SPRINT study and experienced adverse events during follow-up. We are interested i…

基于秩的U型生物标志物风险曲线估计及事件发生时间结局的临界点

Rank-Based Estimation of U-Shaped Biomarker Risk Curves and Critical Points for Time-to-Event Outcomes

研究针对预后生物标志物水平与不良事件风险的U型关系，提出半参数转换模型，通过基于秩的估计和特定估计方法，能在单个生存模型中估计亚组特异性U型风险曲线及其临界点，并应用于UK Biobank数据。

基于树的扫描统计量用于具有时间至事件结局的数据库研究

A tree-based scan statistic for database studies with time-to-event outcomes

提出三种基于树的扫描统计量方法，利用事件时间检测药物不良反应信号，并通过模拟和真实数据库研究验证其优于传统事件计数方法。

在存在治疗停药的肿瘤学RCT中评估时间到事件结果的因果效应

Evaluating causal effects on time-to-event outcomes in an RCT in Oncology with treatment discontinuation

针对肿瘤学RCT中因不良事件导致治疗停药的问题，提出一种贝叶斯主分层框架，将ITT效应分解为主因果效应，以评估治疗停药对生存结果的因果影响，并阐明协变量在提升推断精度和刻画停药行为中的关键作用。

通过功能生存分析进行动态处理效应表型分析

Dynamic treatment effect phenotyping through functional survival analysis

本研究提出基于样条的生存神经网络估计随时间变化的条件治疗效应，并通过功能聚类识别动态治疗效应表型，为个性化医疗选择提供支持。

利用联合累积发生率曲线研究竞争事件

Studying Competing Events with Federated Cumulative Incidence Curves

该研究开发了一种联邦学习方法，通过构建非参数累积发生率曲线，在不共享患者数据的情况下，分析10个站点1万余名癌症患者数据，揭示自身免疫病患者接受免疫检查点抑制剂治疗后的内分泌不良事件风险，为临床提供新依据。