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首页> 外文会议>Joint annual meeting of the International Society of Exposure Science and the International Society for Environmental Epidemiology >Systematic Evaluation of Bias Associated with a Multiple Imputation Approach for Estimating Missing Exposure Data
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Systematic Evaluation of Bias Associated with a Multiple Imputation Approach for Estimating Missing Exposure Data

机译：偏倚的系统评估与估算误差数据的多重插补方法相关

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Introduction: Occupational exposure data frequently include missing or incomplete measurements that, if not addressed, can result in an increased potential for exposure misclassification. Standard approaches for handling missing data, including complete-case analysis and mean substitution, have known limitations. This presents an opportunity to evaluate the use of more advanced statistical approaches, including multiple imputation (Ml). Methods: We systematically evaluated a Ml approach for addressing missing exposure data using a uniquely large and complete dataset of >1 million radiation measurements collected on workers in the skilled trades at three shipyards from 1975-2005. The original dataset contained no missing exposure information and thus represented the true exposure levels in the population. We performed a series of simulations in which 10-99% of the radiation measurements were randomly replaced with missing values and then imputed. To evaluate the performance of Ml, estimates of the mean, median, 25th and 75th percentiles, and variance were calculated for each imputed dataset and compared to the true values of each metric to obtain estimates of the raw and relative bias. Results: For the simulations in which 10-95% of the measurements imputed, the raw bias of the mean ranged from -3.0 to -0.3 (relative bias: 2-15%), the raw bias of the median ranged from -3.0 to 0.0 (relative bias could not be calculated), and the raw bias of the variance ranged from -7.1 to 6.6 (relative bias: 0.1-7.4%). For the simulations in which >95% of the measurements imputed, the magnitude of the biases varied widely for each metric and were not informative. Conclusion: Ml was shown to perform well in characterizing the true distribution of exposures, even when large percentages of radiation measurements were imputed. Our results, combined with the statistical advantages of model-based approaches, support the use of Ml for addressing missing occupational exposure data.
机译：简介：职业接触数据经常包括缺少或不完整的测量，如果不解决，可能会增加潜在的接触错误分类的可能性。处理缺失数据的标准方法（包括完整案例分析和均值替换）具有已知的局限性。这提供了机会来评估更高级的统计方法（包括多重插补（M1））的使用。方法：我们使用1975-2005年期间在三个造船厂的熟练技工的工人上收集的超过100万个辐射测量值的独特大型且完整的数据集，系统地评估了M1方法来解决缺失的暴露数据。原始数据集不包含任何缺失的暴露信息，因此代表了总体中的真实暴露水平。我们进行了一系列模拟，其中10-99％的辐射测量值被随机替换为缺失值，然后进行估算。为了评估M1的性能，针对每个估算数据集计算均值，中位数，第25和第75个百分位数以及方差的估计，并将其与每个度量的真实值进行比较以获得原始和相对偏差的估计。结果：对于其中估算了10-95％的测量值的模拟，平均值的原始偏差范围为-3.0至-0.3（相对偏差：2-15％），中位数的原始偏差范围为-3.0至0.0（无法计算相对偏差），并且方差的原始偏差范围为-7.1至6.6（相对偏差：0.1-7.4％）。对于其中超过95％的测量进行估算的模拟，每个指标的偏差幅度变化很大，并且没有提供信息。结论：即使估算了很大比例的辐射测量值，也显示出M1在表征暴露的真实分布方面表现良好。我们的结果与基于模型的方法的统计优势相结合，支持使用M1来解决缺失的职业暴露数据。

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