Kim Ryung S
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Block 316, 1300 Morris Park Ave, Bronx, NY, 10461, USA,
Eur J Epidemiol. 2015 Mar;30(3):197-207. doi: 10.1007/s10654-014-9974-4. Epub 2014 Dec 2.
Existing literature comparing statistical properties of nested case-control and case-cohort methods have become insufficient for present day epidemiologists. The literature has not reconciled conflicting conclusions about the standard methods. Moreover, a comparison including newly developed methods, such as inverse probability weighting methods, is needed. Two analytical methods for nested case-control studies and six methods for case-cohort studies using proportional hazards regression model were summarized and their statistical properties were compared. The answer to which design and method is more powerful was more nuanced than what was previously reported. For both nested case-control and case-cohort designs, inverse probability weighting methods were more powerful than the standard methods. However, the difference became negligible when the proportion of failure events was very low (<1%) in the full cohort. The comparison between two designs depended on the censoring types and incidence proportion: with random censoring, nested case-control designs coupled with the inverse probability weighting method yielded the highest statistical power among all methods for both designs. With fixed censoring times, there was little difference in efficiency between two designs when inverse probability weighting methods were used; however, the standard case-cohort methods were more powerful than the conditional logistic method for nested case-control designs. As the proportion of failure events in the full cohort became smaller (<10%), nested case-control methods outperformed all case-cohort methods and the choice of analytic methods within each design became less important. When the predictor of interest was binary, the standard case-cohort methods were often more powerful than the conditional logistic method for nested case-control designs.
现有的比较巢式病例对照研究和病例队列研究统计特性的文献，对于当今的流行病学家来说已变得不足。该文献尚未调和关于标准方法的相互矛盾的结论。此外，还需要对包括新开发的方法（如逆概率加权法）进行比较。总结了巢式病例对照研究的两种分析方法和使用比例风险回归模型的病例队列研究的六种方法，并比较了它们的统计特性。哪种设计和方法更具效力的答案比之前报道的更为细微。对于巢式病例对照研究和病例队列研究设计，逆概率加权法都比标准方法更具效力。然而，当全队列中失败事件的比例非常低（<1%）时，这种差异就变得微不足道了。两种设计之间的比较取决于删失类型和发病比例：在随机删失情况下，巢式病例对照设计结合逆概率加权法在两种设计的所有方法中具有最高的统计效力。在固定删失时间的情况下，使用逆概率加权法时两种设计的效率差异不大；然而，对于巢式病例对照设计，标准的病例队列方法比条件logistic方法更具效力。随着全队列中失败事件的比例变小（<10%），巢式病例对照方法优于所有病例队列方法，并且每种设计中分析方法的选择变得不那么重要。当感兴趣的预测变量为二元变量时，对于巢式病例对照设计，标准的病例队列方法通常比条件logistic方法更具效力。
