The case-cohort design, among many two-phase sampling designs, substantially reduces the cost of an epidemiological study by selecting more informative participants within the full cohort for expensive variable measurements. Despite their benefits, additive hazards models, which estimate hazard differences, have rarely been used for the analysis of case-cohort studies due to the lack of software and application examples. In this paper, we describe a newly developed estimation method that fits the additive hazards models to general two-phase sampling studies along with the R package addhazard that implements it. It allows for missing covariates among cases, cohort stratification, robust variances, and the incorporation of auxiliary information from the full cohort to enhance inference precision. We demonstrate the use of this tool to estimate the association of the risk of coronary heart disease (CHD) with biomarkers high-sensitivity C-reactive protein (hs-CRP) and Lipoprotein-associated phospholipase A₂ (Lp-PLA₂) by analyzing the Atherosclerosis Risk in Communities Study, which adopted a two-phase sampling design for studying these two biomarkers. We show that the use of auxiliary variables from the full cohort based on calibration techniques improves the precision of the hazard difference being estimated. We observe a synergistic effect of the two biomarkers among participants with lower LDL cholesterol (LDL-C): the CHD hazard rate attributable to the combined action of high hs-CRP and high Lp-PLA₂ exceeded the sum of the CHD hazard rate attributable to each one independently by 11.58 (95% CI 2.16–21.01) cases per 1000 person-years. With higher LDL-C, we observe the CHD hazard rate attributable to the combined action of high hs-CRP and medium Lp-PLA₂ was less than the sum of their individual effects by 13.42 (95% CI 2.44–24.40) cases per 1000 person-years. This demonstration serves the dual purposes of illustrating analysis techniques and providing insights about the utility of hs-CRP and Lp-PLA₂ for identifying the high-risk population of CHD that the traditional risk factors such as the LDL-C may miss. Epidemiologists are encouraged to use this new tool to analyze other case-cohort studies and incorporate auxiliary variables embedded in the full cohort in their analysis.

在许多两阶段抽样设计中，案例队列设计通过在整个队列中选择更多信息丰富的参与者进行昂贵的变量测量，大大降低了流行病学研究的成本。尽管它们有好处，但由于缺乏软件和应用示例，估计危害差异的附加危害模型很少用于案例队列研究的分析。在本文中，我们描述了一种新开发的估计方法，该方法适用于一般两相抽样研究的附加危害模型以及 R 包addhazard实现它。它允许案例之间缺失的协变量、队列分层、稳健方差以及合并来自完整队列的辅助信息以提高推理精度。我们展示了使用该工具来评估冠心病 (CHD) 风险与生物标志物高敏 C 反应蛋白 (hs-CRP) 和脂蛋白相关磷脂酶 A ₂ (Lp-PLA _{2 ) 的关联}) 通过分析社区研究中的动脉粥样硬化风险，该研究采用两阶段抽样设计来研究这两种生物标志物。我们表明，使用基于校准技术的完整队列中的辅助变量可以提高所估计的风险差异的精度。我们在 LDL 胆固醇 (LDL-C) 较低的参与者中观察到两种生物标志物的协同作用：高 hs-CRP 和高 Lp-PLA ₂的联合作用导致的 CHD 危险率超过了可归因于 CHD 危险率的总和每 1000 人年 11.58 (95% CI 2.16–21.01) 例独立地对每一个。对于较高的 LDL-C，我们观察到高 hs-CRP 和中等 Lp-PLA _{2联合作用导致的冠心病危险率}每 1000 人年比其个体效应之和少 13.42 (95% CI 2.44–24.40) 例。该演示具有双重目的，既说明了分析技术，又提供了有关 hs-CRP 和 Lp-PLA ₂在识别传统风险因素（如 LDL-C）可能遗漏的冠心病高危人群方面的效用的见解。鼓励流行病学家使用这个新工具来分析其他病例队列研究，并在他们的分析中纳入嵌入整个队列的辅助变量。