Control for seasonal variation and time trend in case crossover studies of acute effects of environmental exposures
Control for seasonal variation and time trend in case crossover studies of acute effects of environmental exposures
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DOI：
10.1097/00001648-199909000-00013
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发表时间：
1999-09-01
期刊：
影响因子：
5.4
通讯作者：
Schwartz, J
中科院分区：
文献类型：
--
作者：
Bateson, TF;Schwartz, J
病例交叉研究设计用于研究人群中急性结果的触发因素。它通过设计控制所有测量和未测量的时不变混杂因素。由于遗漏了协变量，对发病环境触发因素的研究可能会因结果的时间趋势而混淆。我们对案例交叉设计通过设计而不是通过建模来控制时间混杂模式的能力进行了模拟研究。我们比较了五种案例交叉控制抽样策略，包括匹配对、对称双向、总历史方法和 Navidi 提出的两种方法（Biometrics 1998；54：596-605）。我们模拟了 1.10 和 2.00 的真实相对风险 (RR)，并通过季节性模式以及线性和非线性长期趋势引入混杂因素，得出高达 3.18 的估计 RR 值。对称双向方法在四个滞后时间内进行比较，并在滞后最短时控制时间混杂效果最好。由于存在 1 周的滞后，估计 RR 值为 1.10 和 2.01。其他四种方法未能控制时间趋势。我们的模拟表明，对称双向案例交叉设计可以通过设计充分控制时间混杂，尽管它不如泊松回归分析有效（66％）。
The case-crossover study design is used to study the triggers of acute outcomes in populations. It controls for all measured and unmeasured time-invariant confounders by design. Studies of environmental triggers of morbidity are potentially confounded by temporal trends in the outcome owing to omitted covariates. We conducted a simulation study of the case-crossover design's ability to control for temporal confounding patterns by design rather than through modeling. We compared five case-crossover control sampling strategies including the matched pair, a symmetric bi-directional, a total history approach, and two approaches proposed by Navidi (Biometrics 1998;54:596-605). We simulated true relative risks (RR) of 1.10 and 2.00 and induced confounding by seasonal patterns as well as linear and nonlinear long-term trends to yield estimated RR values as high as 3.18. The symmetric bi-directional approach was compared across four lag times and controlled for temporal confounding best when the lag was shortest. With a 1-week lag, it estimated the RR values as 1.10 and 2.01. The four other approaches failed to control for the temporal trends. Our simulations show that the symmetric bi-directional case-crossover design can substantially control for temporal confounding by design although it is not as efficient (66%) as Poisson regression analysis.