Estimating Risk With Time-to-Event Data: An Application to the Women’s Health Initiative
事件发生时间数据的风险估计：妇女健康倡议应用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要涉及生存分析与疾病绝对风险，未直接对应给定的金融风险或其他计量领域细分。
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事件发生时间数据的风险估计：妇女健康倡议应用
- 研究问题
- 罕见疾病在队列中事件稀少时，如何更有效估计随时间变化的个体绝对风险？摘要关注仅用队列估计基线风险的不稳定性。
- 方法与思路
- 作者推导外部疾病发生率与基线风险的关系，将外部信息纳入绝对风险估计，同时允许队列与外部来源的发生率存在差异。
- 材料陈述的贡献
- 摘要称，估计量具有一致收敛和弱收敛性质，模拟中较不使用外部信息的 Breslow 方法更有效率，并提供大型队列应用。
- 阅读关注点
- 应核对外部与队列发生率差异如何建模、删失及随访条件，并查看外部信息误差是否计入绝对风险估计的不确定性。
- 阅读边界
- 依据版本未注明的 OpenAlex 索引摘要；外部信息兼容条件、删失假设和效率增益未给出，疾病风险不属于金融尾部风险。
原文摘要
Accurate and individualized risk prediction is critical for population control of chronic diseases such as cancer and cardiovascular disease. Large cohort studies provide valuable resources for building risk prediction models, as the risk factors are collected at the baseline and subjects are followed over time until disease occurrence or termination of the study. However, for rare diseases the baseline risk may not be estimated reliably based on cohort data only, due to sparse events. In this paper, we propose to make use of external information to improve efficiency for estimating time-dependent absolute risk. We derive the relationship between external disease incidence rates and the baseline risk, and incorporate the external disease incidence information into estimation of absolute risks, while allowing for potential difference of disease incidence rates between cohort and external sources. The asymptotic properties, namely, uniform consistency and weak convergence, of the proposed estimators are established. Simulation results show that the proposed estimator for absolute risk is more efficient than that based on the Breslow estimator, which does not utilize external disease incidence rates. A large cohort study, the Women's Health Initiative Observational Study, is used to illustrate the proposed method.