Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.
Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.
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DOI：
10.1186/1471-2288-14-122
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发表时间：
2014-11-24
影响因子：
4
通讯作者：
Tobias A
中科院分区：
文献类型：
--
作者：
Armstrong BG;Gasparrini A;Tobias A
时间分层病例交叉方法是传统时间序列回归的流行替代方法，用于分析环境暴露时间序列（空气污染、天气）与健康结果计数之间的关联。这些几乎总是使用条件逻辑回归对扩展为病例-对照（病例交叉）格式的数据进行分析，但这有一些局限性。特别是调整计数中的过色散和自相关是不可能的。已经确定，带地层指标的泊松计数模型与条件逻辑回归的计数模型给出了相同的估计，并且没有这些限制，但它很少使用，可能是因为估计许多地层参数的开销。与标准无条件泊松模型相比，条件泊松模型通过限定每一层的事件总数来避免估计地层参数，从而简化了计算，增加了可拟合的地层数量。与条件逻辑模型不同，条件泊松模型不需要扩展数据，并且可以对过分散和自相关进行调整。它可以在Stata、R和其他软件包中使用。通过对一些真实数据的应用和模拟，我们证明了条件泊松模型比条件逻辑分析更容易编码，运行时间更短，并且比标准泊松模型更适合于更大的数据集。允许过度分散或自相关与条件泊松模型是可能的，但当不需要这个模型给出相同的估计从条件逻辑回归。条件泊松回归模型为分层时间序列数据的案例交叉分析提供了一种替代方法，具有一定的优势。条件泊松模型也可用于其他情况下，其中主要控制的混淆是通过精细分层。本文的在线版本（doi:10.1186/1471-2288-14-122）包含补充材料，可供授权用户使用。
The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case–control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification. The online version of this article (doi:10.1186/1471-2288-14-122) contains supplementary material, which is available to authorized users.
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