Inverse propensity weighting to adjust for bias in fatal crash samples.
Inverse propensity weighting to adjust for bias in fatal crash samples.
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用于调整致命事故样本偏差的逆倾向权重。
DOI：
10.1016/j.aap.2012.09.025
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Hannan,EdwardL
中科院分区：
文献类型：
--
作者：
Clark,DavidE;Hannan,EdwardL
死亡分析报告系统（法尔斯）拥有来自美国所有地区的数据，但仅限于致命的撞车事故。国家汽车抽样系统-一般估计系统（NASS-GES）包括所有类型的严重交通事故，但仅限于少数几个抽样地区。结合这两个样本的优势可能会抵消其limitation.METHODSLogistic回归（允许样本设计，并在选定的人，事件，和地理水平的因素为条件）被用来确定的倾向（PFC）为每个受伤的人在2002-2008年的NASS-GES数据是在一个致命的碰撞样本。在致命车祸中受伤的NASS-GES受试者然后通过WFC=（1/PFC）的因子重新加权以创建“假群体”。在2007年法尔斯数据中，还将来自NASS-GES的权重（WFC）应用于受伤受试者，以创建另一个假人群。将这些人工假种群的特征和死亡率预测与使用原始NASS-GES样本获得的特征和死亡率预测进行比较。法尔斯情况下的总的WFC也被用来估计农村和城市地区的碰撞伤害的数量，并与独立报道的数据进行比较。与使用原始NASS-GES样本的回归结果相比，基于致命碰撞样本的未经调整的模型对受伤受试者的死亡率的协变量影响的估计不准确。使用WFC重新加权后，基于假种群的估计值与使用原始NASS-GES样本获得的结果相似。法尔斯情况下的总WFCfor合理的估计在农村和城市地区的碰撞伤害的数量，并提供了一个估计的农村的影响后，控制其他factors.CONCLUSIONSWeights来自分析的NASS-GES数据（选择成一个致命的碰撞样本的逆倾向）允许适当的调整选择偏见在致命的碰撞样本，包括法尔斯。
BACKGROUNDThe Fatality Analysis Reporting System (FARS) has data from all areas of the United States, but is limited to fatal crashes. The National Automotive Sampling System–General Estimates System (NASS–GES) includes all types of serious traffic crashes, but is limited to a few sampling areas. Combining the strengths of these two samples might offset their limitations.METHODSLogistic regression (allowing for sample design, and conditional upon selected person-, event-, and geographic-level factors) was used to determine the propensity (PFC) for each injured person in 2002–2008 NASS–GES data to be in a fatal crash sample. NASS–GES subjects injured in fatal crashes were then reweighted by a factor of WFC=(1/PFC) to create a “pseudopopulation”. The weights (WFC) derived from NASS–GES were also applied to injured subjects in 2007 FARS data to create another pseudopopulation. Characteristics and mortality predictions from these artificial pseudopopulations were compared to those obtained using the original NASS–GES sample. The sum of WFCfor FARS cases was also used to estimate the number of crash injuries for rural and urban locations, and compared to independently reported data.RESULTSCompared to regression results using the original NASS–GES sample, unadjusted models based on fatal crash samples gave inaccurate estimates of covariate effects on mortality for injured subjects. After reweighting using WFC, estimates based upon the pseudopopulations were similar to results obtained using the original NASS–GES sample. The sum of WFCfor FARS cases gave reasonable estimates for the number of crash injuries in rural and urban locations, and provided an estimate of the rural effect on mortality after controlling for other factors.CONCLUSIONSWeights derived from analysis of NASS–GES data (the inverse propensity for selection into a fatal crash sample) allow appropriate adjustment for selection bias in fatal crash samples, including FARS.
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影响因子：
5.9
作者：
Clark, DE
通讯作者：
Clark, DE
更正 FARS 数据中的样本选择以估计安全带的使用情况。
影响因子：
4.1
作者：
S. Islam;F. Goetzke
通讯作者：
F. Goetzke
使用倾向评分减少椎体骨折比值比的抽样偏差
影响因子：
4
作者：
Ying Lu;H. Jin;H. Jin;M.;C. Glüer
通讯作者：
C. Glüer
样本调查数据的Logistic回归分析
DOI：
--
发表时间：
1987
期刊：
影响因子：
--
作者：
G. Roberts;N. K. Rao;Sanjeev Kumar
通讯作者：
Sanjeev Kumar
双对比较——一种确定乘员特征如何影响交通事故死亡风险的新方法。
影响因子：
5.9
作者：
Leonard Evans
通讯作者：
Leonard Evans