A distribution-based multiple imputation method for handling bivariate pesticide data with values below the limit of detection.
A distribution-based multiple imputation method for handling bivariate pesticide data with values below the limit of detection.
复制标题
DOI：
10.1289/ehp.1002124
复制
发表时间：
2011-03
影响因子：
10.4
通讯作者：
Arcury TA
中科院分区：
文献类型：
--
作者：
Chen H;Quandt SA;Grzywacz JG;Arcury TA
环境和生物医学研究人员经常遇到实验室数据受到检测下限（LOD）的限制。处理这些左删失数据的常用方法，例如简单地将< LOD的所有值替换为常数，可能会使参数估计产生偏差。相比之下，多重插补（MI）方法可产生有效和稳健的参数估计值，并为可作为结局或预测因子进行分析的变量提供明确的插补值。在这篇文章中，我们扩展了基于分布的MI方法左删失数据的双变量设置，具体来说，在两个时间点的生物措施的纵向研究。我们已经提出了一个二元正态分布的似然函数，考虑到< LOD的值以及假设随机缺失的缺失数据，我们使用估计的分布参数来插补< LOD的值，并通过标准统计方法生成多个合理的数据集进行分析。我们进行了一项模拟研究，以评估抽样性能的估计，我们说明了一个实际应用的数据，从社区预测方法来衡量农民农药暴露（PACE3）的研究，估计尿乙酰甲胺磷（APE）浓度（农药暴露）在两个时间点和自我报告的症状之间的关联。模拟研究结果表明，插补值和观察值一起与假设和估计的基础分布一致。我们对PACE3数据的分析使用MI来插补< LOD的APE值，结果显示尿APE浓度与潜在的农药中毒症状显著相关。基于简单替代方法的结果与基于MI方法的结果有很大不同。基于分布的MI方法是一种有效可行的方法来分析值< LOD的二元数据，特别是当需要明确的未检测值时。我们建议在环境和生物医学研究中使用这种方法。
Environmental and biomedical researchers frequently encounter laboratory data constrained by a lower limit of detection (LOD). Commonly used methods to address these left-censored data, such as simple substitution of a constant for all values < LOD, may bias parameter estimation. In contrast, multiple imputation (MI) methods yield valid and robust parameter estimates and explicit imputed values for variables that can be analyzed as outcomes or predictors. In this article we expand distribution-based MI methods for left-censored data to a bivariate setting, specifically, a longitudinal study with biological measures at two points in time. We have presented the likelihood function for a bivariate normal distribution taking into account values < LOD as well as missing data assumed missing at random, and we use the estimated distributional parameters to impute values < LOD and to generate multiple plausible data sets for analysis by standard statistical methods. We conducted a simulation study to evaluate the sampling properties of the estimators, and we illustrate a practical application using data from the Community Participatory Approach to Measuring Farmworker Pesticide Exposure (PACE3) study to estimate associations between urinary acephate (APE) concentrations (indicating pesticide exposure) at two points in time and self-reported symptoms. Simulation study results demonstrated that imputed and observed values together were consistent with the assumed and estimated underlying distribution. Our analysis of PACE3 data using MI to impute APE values < LOD showed that urinary APE concentration was significantly associated with potential pesticide poisoning symptoms. Results based on simple substitution methods were substantially different from those based on the MI method. The distribution-based MI method is a valid and feasible approach to analyze bivariate data with values < LOD, especially when explicit values for the nondetections are needed. We recommend the use of this approach in environmental and biomedical research.
登录
查看更多内容
年轻墨西哥裔美国儿童的有机磷酸农药暴露和神经发育。
影响因子：
10.4
作者：
Eskenazi, Brenda;Marks, Amy R.;Bradman, Asa;Harley, Kim;Barr, Dana B.;Johnson, Caroline;Morga, Norma;Jewell, Nicholas A.
通讯作者：
Jewell, Nicholas A.
影响因子：
10.4
作者：
Lubin JH;Colt JS;Camann D;Davis S;Cerhan JR;Severson RK;Bernstein L;Hartge P
通讯作者：
Hartge P
影响因子：
3.1
作者：
Helsel, Dennis R.
通讯作者：
Helsel, Dennis R.
影响因子：
2
作者：
Lyles, RH;Fan, DJ;Chuachoowong, R
通讯作者：
Chuachoowong, R
影响因子：
8.8
作者：
Baccarelli, A;Pfeiffer, R;Landi, MT
通讯作者：
Landi, MT