Ramsay Timothy, Burnett Richard, Krewski Daniel
R. Samuel McLaughlin Centre for Population Health Risk Assessment, Ottawa, Ontario, Canada.
Environ Health Perspect. 2003 Aug;111(10):1283-8. doi: 10.1289/ehp.6047.
During the past few years, the generalized additive model (GAM) has become a standard tool for epidemiologic analysis exploring the effect of air pollution on population health. Recently, the use of the GAM has been extended from time-series data to spatial data. Still more recently, it has been suggested that the use of GAMs to analyze time-series data results in air pollution risk estimates being biased upward and that concurvity in the time-series data results in standard error estimates being biased downward. We show that concurvity in spatial data can lead to underestimation of the standard error of the estimated air pollution effect, even when using an asymptotically unbiased standard error estimator. We also show that both the magnitude and direction of the bias in the air pollution effect depend, at least in part, on the nature of the concurvity. We argue that including a nonparametric function of location in a GAM for spatial epidemiologic data can be expected to result in concurvity. As a result, we recommend caution in using the GAM to analyze this type of data.
在过去几年中，广义相加模型（GAM）已成为探索空气污染对人群健康影响的流行病学分析的标准工具。最近，GAM的应用已从时间序列数据扩展到空间数据。更近一些时候，有人提出使用GAM分析时间序列数据会导致空气污染风险估计值向上偏差，并且时间序列数据中的共曲线性会导致标准误差估计值向下偏差。我们表明，即使使用渐近无偏标准误差估计器，空间数据中的共曲线性也会导致估计的空气污染效应的标准误差被低估。我们还表明，空气污染效应偏差的大小和方向至少部分取决于共曲线性的性质。我们认为，在用于空间流行病学数据的GAM中纳入位置的非参数函数可能会导致共曲线性。因此，我们建议在使用GAM分析此类数据时要谨慎。
