Center for Environmental Health and Technology, Brown University, Providence, Rhode Island, USA.
Am J Epidemiol. 2011 Jul 1;174(1):118-24. doi: 10.1093/aje/kwr038. Epub 2011 May 3.
The case-crossover method is an efficient study design for evaluating associations between transient exposures and the onset of acute events. In one common implementation of this design, odds ratios are estimated using conditional logistic or stratified Cox proportional hazards models, with data stratified on each individual event. In environmental epidemiology, where aggregate time-series data are often used, combining strata with identical exposure histories may be computationally convenient. However, when the SAS software package (SAS Institute Inc., Cary, North Carolina) is used for analysis, users can obtain biased results if care is not taken to properly account for multiple cases observed at the same time. The authors show that fitting a stratified Cox model with the "Breslow" option for handling tied failure times (i.e., ties = Breslow) provides unbiased health-effects estimates in case-crossover studies with shared exposures. The authors' simulations showed that using conditional logistic regression-or equivalently a stratified Cox model with the "ties = discrete" option-in this setting leads to health-effect estimates which can be biased away from the null hypothesis of no association by 22%-39%, even for small simulated relative risks. All methods tested by the authors yielded unbiased results under a simulated scenario with a relative risk of 1.0. This potential bias does not arise in R (R Foundation for Statistical Computing, Vienna, Austria) or Stata (Stata Corporation, College Station, Texas).
病例交叉研究方法是一种高效的研究设计，可用于评估短暂暴露与急性事件发病之间的关联。在该设计的一种常见实现中，使用条件逻辑回归或分层 Cox 比例风险模型来估计比值比，数据按每个个体事件分层。在环境流行病学中，通常使用综合时间序列数据，对具有相同暴露史的层进行组合在计算上可能很方便。但是，如果不注意正确考虑同时观察到的多个病例，使用 SAS 软件包（SAS Institute Inc.，Cary，North Carolina）进行分析时，用户可能会得到有偏的结果。作者表明，在具有共享暴露的病例交叉研究中，使用“Breslow”选项处理捆绑失效时间的分层 Cox 模型（即 ties = Breslow）可以提供无偏的健康效应估计。作者的模拟结果表明，在这种情况下使用条件逻辑回归——或者等效地使用“ties = discrete”选项的分层 Cox 模型——会导致健康效应估计值偏向于没有关联的零假设，即使对于模拟的相对风险较小也是如此，可偏离 22%-39%。作者测试的所有方法在相对风险为 1.0 的模拟场景下均产生无偏结果。在 R（奥地利维也纳的 R 基金会统计计算）或 Stata（德克萨斯州立大学站的 Stata 公司）中不会出现这种潜在偏差。
