On the equivalence of case-crossover and time series methods in environmental epidemiology
On the equivalence of case-crossover and time series methods in environmental epidemiology
复制标题
DOI：
10.1093/biostatistics/kxl013
复制
发表时间：
2007-04-01
期刊：
影响因子：
2.1
通讯作者：
Zeger, Scott L.
中科院分区：
文献类型：
--
作者：
Lu, Yun;Zeger, Scott L.
病例交叉设计是15年前在流行病学中引入的，作为一种仅使用病例研究风险因素对健康事件影响的方法。这个想法是将病例在病例定义事件之前或期间的暴露与同一个人在其他类似“参考”时间的暴露进行比较。分析每日暴露和仅病例数据的另一种方法是时间序列分析。这里，对数线性回归模型将每天的预期事件总数表示为暴露水平和潜在混杂变量的函数。在大气污染时间序列分析中，时间和天气的平滑函数是主要的混杂因素。时间序列和案例交叉方法通常被视为竞争方法。在本文中，我们表明，使用条件Logistic回归的情况下，交叉是一个特殊的情况下，时间序列分析时，有一个共同的曝光，如在空气污染的研究。这种等价性为案例交叉分析和更好地理解时间序列模型提供了计算便利。时间序列对数线性回归解释了泊松方差的过度分散，而病例交叉分析通常不会。这种等价性还允许使用标准对数线性模型诊断对病例交叉数据进行模型检查。
The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.