Adaption of bivariate frailty models for prediction, with application to biological markers as prognostic indicators
适用于预测的二元脆弱性模型：生物标志物预后应用AI 解读 ↓
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适用于预测的二元脆弱性模型：生物标志物预后应用
- 研究问题
- 观察到生物标志物变化时，如何更新个体未来事件风险，并在适合预测的删失过程中评估标志物的预后信息？
- 方法与思路
- 作者调整脆弱性模型以适应预测场景的删失过程，提出预测导向检验及估计程序，通过汇总各地标分析对应的鞅实现。
- 材料陈述的贡献
- 摘要称方法针对现有脆弱性分析中不适合预测的删失假设作出调整，并提供面向标志物出现后风险更新的检验与估计。
- 阅读关注点
- 核对预测时点和可用信息、相关删失过程的设定，以及并行地标分析的鞅如何汇总并形成有效检验和估计。
- 阅读边界
- 仅据 OpenAlex 索引摘要，版本未注明；摘要没有预测性能、临床决策阈值或干预效果，不能将风险预测解释为因果效应。
原文摘要
Serial biological markers are often used in medicine as prognostic indicators. For example, elevated tumour antigen tests presage tumour recurrence; rapid decrease in the CD4 cell counts of HIV + individuals indicate high risk for developing AIDS. Methods for evaluating the usefulness of markers should address how the prediction of future risk is revised, in a way that assists the making of decisions at the time such an indicator is observed. Proportional hazards modelling with time-varying covariates, a common approach, seems unnatural for this purpose because its orientation is explanatory; it addresses the question ‘Is the individual suffering a failure more likely to have had the marker?’, and allows use of all information prior to the failure, rather than the more relevant predictive question ‘Does the appearance of the marker alter the individual's subsequent risk enough to warrant intervention today?’. Frailty models do address this question, but available methods for their analysis make censoring assumptions which are inappropriate in the predictive setting. We adapt frailty models to relevant censoring processes, and propose a predictively oriented test statistic and a related estimation procedure based on summing concurrent martingales, each representing a ‘landmark’ analysis.