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目的:系统介绍病例队列研究设计的基本原理，以及风险比（n HR）的常用估计方法及其应用。n 方法:首先，介绍病例队列研究设计的基本原理；其次，对Prentice法、Self-Prentice法和Barlow法加权Cox比例风险回归模型进行描述和说明；最后，以上海市女性健康队列研究为例，分析全队列数据与病例队列样本中肥胖与肝癌发病的关联，并进一步比较两者在各种模型中参数估计的结果。结果:在全队列数据和病例队列样本中，发现肥胖与女性肝癌发病的关联均有统计学意义。在Cox比例风险回归模型中，全队列数据和病例队列样本的回归系数（n β）随着协变量调整有所波动，但是两者的n HR值较为接近；两者n β的标准误存在差异，即病例队列样本n β的标准误大于全队列的参数估计值，n HR值的95%n CI更宽。在加权Cox比例风险回归模型中，Prentice法相比Self-Prentice法和Barlow法的n β的标准误更接近全队列的参数估计值，n HR值的95%n CI更靠近全队列的结果。n 结论:病例队列研究设计通过收集和分析子队列成员和发病者的资料，可以获得接近全队列的参数结果，同时能够节约样本量和提高研究效率。此外，在病例队列设计中可以优先选择Prentice法。“,”Objective:To systematically introduce the design of case-cohort study and the statistical methods of relative risk estimation and their application in the design.Methods:First, we introduced the basic principles of case-cohort study design. Secondly, Prentice\'s method, Self-Prentice method and Barlow method were described in the weighted Cox proportional hazard regression models in detail, finally, the data from the Shanghai Women\'s Health Study were used as an example to analyze the association between obesity and liver cancer incidence in the full cohort and case-cohort sample, and the results of parameters from each method were compared.Results:Significant association was observed between obesity and risk for liver cancer incidence in women in both the full cohort and the case-cohort sample. In the Cox proportional hazard regression model, the partial regression coefficients of the full cohort and the case-cohort sample fluctuated with the adjustment of confounding factors, but the hazard ratio estimates of them were close. There was a difference in the standard error of the partial regression coefficient between the full cohort and the case-cohort sample. The standard error of the partial regression coefficient of the case-cohort sample was larger than that of the full cohort, resulting in a wider 95% confidence interval of the relative risk. In the weighted Cox proportional hazard regression model, the standard error of the partial regression coefficient of Prentice\'s method was closer to the parameter estimates from full cohort than Self-Prentice method and Barlow method, and the 95% confidence interval of hazard ratio was closer to that of the full cohort.Conclusions:Case-cohort design could yield parameter results closer to the full cohort by collecting and analyzing data from sub-cohort members and patients with the disease, and reduce sample size and improve research efficiency. The results suggested that Prentice\'s method would be preferred in case-cohort design.