发表机构
University of Bologna; Leiden University（博洛尼亚大学; 莱顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对纵向数据下的生存时间动态预测，提出基于惩罚回归校准与保形校准的预测区间方法，相比朴素反转更稳定可靠。
AI 中文摘要
大多数生存预测研究侧重于估计生存概率，而非预测个体事件时间。近期的保形方法已使得在右删失结局下构建生存时间的预测区间成为可能，但现有方法仅限于仅在基线时测量的协变量，并未涉及纵向数据下的动态预测。我们在一个具有纵向协变量的动态预测框架中研究生存时间的预测区间。我们的方法采用惩罚回归校准（PRC）作为工作动态预测模型，将纵向历史的线性混合模型与地标后生存的Cox模型相结合，然后应用保形校准步骤以获得预测区间。我们将通过直接反转由PRC估计的生存函数获得的朴素区间与我们的动态保形方法进行比较。一项蒙特卡洛模拟研究评估了不同样本量、删失水平、地标时间以及非比例风险（NPH）情景下的经验覆盖率和区间长度。我们通过在ADNI数据集中计算直至痴呆诊断时间的动态预测区间来展示所提出的方法。结果表明，朴素反转通常不可靠，而所提出的动态保形方法产生更稳定的预测性能。
英文摘要
Most work on survival prediction focuses on estimating survival probabilities rather than predicting individual event times. Recent conformal methods have made it possible to construct prediction intervals for survival times with right-censored outcomes, but existing approaches are restricted to settings with covariates only measured at baseline and do not address dynamic prediction with longitudinal data. We study prediction intervals for survival times in a dynamic prediction framework with longitudinal covariates. Our approach uses Penalized Regression Calibration (PRC) as a working dynamic prediction model, combining linear mixed models for the longitudinal histories with a Cox model for post-landmark survival, and then applies a conformal calibration step to obtain prediction intervals. We compare naive intervals obtained by direct inversion of the survival function estimated by PRC to our dynamic conformal method. A Monte Carlo simulation study evaluates empirical coverage and interval length across sample sizes, censoring levels, landmark times, and non-proportional hazards (NPH) scenarios. We illustrate the proposed methodology by computing dynamic prediction intervals for the time until a dementia diagnosis in the ADNI dataset. The results show that naive inversion is often unreliable, whereas the proposed dynamic conformal method yields more stable predictive performance.
Comments20 pages. Supplementary material included
