Case-crossover analyses of air pollution exposure data - Referent selection strategies and their implications for bias
Case-crossover analyses of air pollution exposure data - Referent selection strategies and their implications for bias
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DOI：
10.1097/01.ede.0000181315.18836.9d
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发表时间：
2005-11-01
期刊：
影响因子：
5.4
通讯作者：
Lumley, T
中科院分区：
文献类型：
--
作者：
Janes, H;Sheppard, L;Lumley, T
病例交叉设计已被广泛用于研究短期空气污染暴露与急性不良健康事件风险之间的关系。该设计仅使用案例;对于每个单独的案例，将事件发生前的暴露与其他控制（或"参考"）时间的暴露进行比较。通过进行受试者内比较来控制时不变混杂因素。在空气污染环境中更重要的是，时变混杂因素也可以通过设计控制，方法是将参考物与指数时间相匹配。参照物选择策略之所以重要，除了控制混杂之外。病例交叉设计隐含假设在所指时间内暴露量无趋势。此外，所使用的统计方法-条件逻辑回归-是公正的，只有与某些参考策略。我们在这里回顾的情况下，交叉文献中的空气污染的背景下，专注于参考选择的关键问题。最后，我们提出了一系列建议，以选择一个参考策略与空气污染暴露数据。具体来说，我们提倡时间分层的方法来选择参考，因为它确保了无偏的条件logistic回归估计，避免了暴露序列中时间趋势造成的偏倚，并且可以根据特定的时变混杂因素进行调整。
The case-crossover design has been widely used to study the association between short-term air pollution exposure and the risk of an acute adverse health event. The design uses cases only; for each individual case, exposure just before the event is compared with exposure at other control (or "referent") times. Time-invariant confounders are controlled by making within-subject comparisons. Even more important in the air pollution setting is that time-varying confounders can also be controlled by design by matching referents to the index time. The referent selection strategy is important for reasons in addition to control of confounding. The case-crossover design makes the implicit assumption that there is no trend in exposure across the referent times. In addition, the statistical method that is used-conditional logistic regression-is unbiased only with certain referent strategies. We review here the case-crossover literature in the air pollution context, focusing on key issues regarding referent selection. We conclude with a set of recommendations for choosing a referent strategy with air pollution exposure data. Specifically, we advocate the time-stratified approach to referent selection because it ensures unbiased conditional logistic regression estimates, avoids bias resulting from time trend in the exposure series, and can be tailored to match on specific time-varying confounders.