基于秩的U型生物标志物风险曲线估计及事件发生时间结局的临界点
Rank-Based Estimation of U-Shaped Biomarker Risk Curves and Critical Points for Time-to-Event Outcomes
AI总结：
研究针对预后生物标志物水平与不良事件风险的U型关系，提出半参数转换模型，通过基于秩的估计和特定估计方法，能在单个生存模型中估计亚组特异性U型风险曲线及其临界点，并应用于UK Biobank数据。
AI中文摘要：
在多种疾病中，预后生物标志物水平与不良事件风险之间常见U型关系，低和高生物标志物值均与风险升高相关，有明确的最低点即临界点标志最低风险的生物标志物值。U型风险曲线尤其是临界点位置有助于识别高低风险亚组。但现有方法有限，U型风险模型很少考虑生存结局，现有生存分析方法无法估计或正式推断临界点。为此提出半参数转换模型，明确参数化临界点，用基于秩的最大C指数估计参数部分，用平滑Kaplan-Meier估计非参数部分。该框架能在单个生存模型中估计亚组特异性U型风险曲线及其临界点。通过数值研究建立了估计量的一致性和渐近正态性，并展示了有限样本性能。将该方法应用于英国生物银行数据，以表征体重指数与全因死亡率之间亚组特异性U型关联并识别相应临界点。
英文摘要：
U-shaped relationships between prognostic biomarker levels and adverse event risk are commonly observed across diseases, where both low and high biomarker values are associated with elevated risk, with a well-defined minimum -- the critical point -- marking the biomarker value of the lowest risk. The U-shaped risk curve, especially the location of the critical point, informs the identification of high- and low-risk subgroups. However, existing methods are limited: U-shaped risk models rarely accommodate survival outcomes, and existing survival analysis methods do not enable estimation of or formal inference for the critical point. To fill this gap, we propose a semiparametric transformation model that explicitly parameterizes the critical point, a rank-based maximum C-index estimator for the parametric component, and a smoothed Kaplan-Meier estimation approach for the nonparametric component. The resulting framework estimates both subgroup-specific U-shaped risk curves and their critical points within a single survival model. We establish consistency and asymptotic normality of the proposed estimators and demonstrate their finite-sample performance through numerical studies. We apply the proposed methods to UK Biobank data to characterize subgroup-specific U-shaped associations between body mass index and all-cause mortality and to identify the corresponding critical points.
