Burstyn Igor, Cherry Nicola M, Yasui Yutaka, Kim Hyang-Mi
Community and Occupational Medicine Program, Department of Medicine, Faculty of Medicine and Dentistry, The University of Alberta, Edmonton, Alberta, Canada.
BMC Med Res Methodol. 2008 Jul 4;8:43. doi: 10.1186/1471-2288-8-43.
The main objective of this paper is to compare different methods for predicting the levels of SO2 air pollution in oil and gas producing area of rural western Canada. Month-long average air quality measurements were collected over a two-year period (2001-2002) at multiple locations, with some side-by-side measurements, and repeated time-series at selected locations.
We explored how accurately location-specific mean concentrations of SO2 can be predicted for 2002 at 666 locations with multiple measurements. Means of repeated measurements on the 666 locations in 2002 were used as the alloyed gold standard (AGS). First, we considered two approaches: one that uses one measurement from each location of interest; and the other that uses context data on proximity of monitoring sites to putative sources of emission in 2002. Second, we imagined that all of the previous year's (2001's) data were also available to exposure assessors: 9,464 measurements and their context (month, proximity to sources). Exposure prediction approaches we explored with the 2001 data included regression modeling using either mixed or fixed effects models. Third, we used Bayesian methods to combine single measurements from locations in 2002 (not used to calculate AGS) with different priors.
The regression method that included both fixed and random effects for prediction (Best Linear Unbiased Predictor) had the best agreement with the AGS (Pearson correlation 0.77) and the smallest mean squared error (MSE: 0.03). The second best method in terms of correlation with AGS (0.74) and MSE (0.09) was the Bayesian method that uses normal mixture prior derived from predictions of the 2001 mixed effects applied in the 2002 context.
It is likely that either collecting some measurements from the desired locations and time periods or predictions of a reasonable empirical mixed effects model perhaps is sufficient in most epidemiological applications. The method to be used in any specific investigation will depend on how much uncertainty can be tolerated in exposure assessment and how closely available data matches circumstances for which estimates/predictions are required.
本文的主要目的是比较加拿大西部农村油气产区二氧化硫空气污染水平的不同预测方法。在两年期间（2001 - 2002年）在多个地点收集了长达一个月的空气质量测量数据，有一些是并排测量，并且在选定地点进行了重复时间序列测量。
我们探讨了如何准确预测2002年666个有多次测量的地点特定的二氧化硫平均浓度。2002年666个地点重复测量的平均值用作合金化金标准（AGS）。首先，我们考虑了两种方法：一种是使用每个感兴趣地点的一次测量；另一种是使用2002年监测站点与假定排放源接近程度的背景数据。其次，我们设想暴露评估人员也可以获得上一年（2001年）的所有数据：9464次测量及其背景（月份、与源的接近程度）。我们用2001年数据探索的暴露预测方法包括使用混合或固定效应模型的回归建模。第三，我们使用贝叶斯方法将2002年地点的单次测量（未用于计算AGS）与不同先验结合起来。
用于预测的包含固定和随机效应的回归方法（最佳线性无偏预测器）与AGS的一致性最好（皮尔逊相关系数0.77），平均平方误差最小（MSE：0.03）。在与AGS的相关性（0.74）和MSE（0.09）方面第二好的方法是使用从2001年混合效应在2002年背景下的预测得出的正态混合先验的贝叶斯方法。
在大多数流行病学应用中，从期望的地点和时间段收集一些测量数据或合理的经验混合效应模型的预测可能就足够了。在任何特定调查中使用的方法将取决于暴露评估中可以容忍多少不确定性以及现有数据与需要估计/预测的情况的匹配程度。
