Problem
Research questions and friction points this paper is trying to address.
survival times
dynamic prediction
longitudinal covariates
Dynamic prediction intervals for survival times
🤖 AI Summary
本文针对生存时间预测问题，提出了一种结合纵向协变量的动态预测区间方法，使用惩罚回归校准模型和一致性校准步骤来解决右删失数据下的个体事件时间预测。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic Prediction
Conformal Calibration
Penalized Regression Calibration
Longitudinal Covariates
Survival Analysis
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