Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging
利用贝叶斯模型平均组合纵向与事件时间联合模型的动态预测AI 解读 ↓
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利用贝叶斯模型平均组合纵向与事件时间联合模型的动态预测
- 研究问题
- 纵向指标与事件时间之间的关联结构会影响个体动态预测；如何组合不同关联结构的联合模型，改善随访过程中的预测准确性？
- 方法与思路
- 作者使用贝叶斯模型平均组合不同联合模型，使模型权重同时依赖个体与随访时间，并随新纵向观测更新生存概率或未来响应预测。
- 材料陈述的贡献
- 摘要强调关联结构设定对预测的重要性，并提出个体和时间依赖的模型权重，使同一受试者在不同随访时点可由不同模型主导预测。
- 阅读关注点
- 应核对候选关联结构、动态权重的计算信息集和更新方式，查看预测准确性的评价设计，以及模型不确定性如何进入预测区间。
- 阅读边界
- 依据稿件版本未注明的 OpenAlex 索引摘要。预测改善的数值、评价样本及关联结构设定未说明，不能据此判断普遍优越性或推断刊本变化。
原文摘要
The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in recent years. More recently, a new and attractive application of this type of model has been to obtain individualized predictions of survival probabilities and/or of future longitudinal responses. The advantageous feature of these predictions is that they are dynamically updated as extra longitudinal responses are collected for the subjects of interest, providing real time risk assessment using all recorded information. The aim of this article is two-fold. First, to highlight the importance of modeling the association structure between the longitudinal and event time responses that can greatly influence the derived predictions, and second, to illustrate how we can improve the accuracy of the derived predictions by suitably combining joint models with different association structures. The second goal is achieved using Bayesian model averaging, which, in this setting, has the very intriguing feature that the model weights are not fixed but they are rather subject- and time-dependent, implying that at different follow-up times predictions for the same subject may be based on different models. Supplementary materials for this article are available online.