Imputations of missing values in practice: Results from imputations of serum cholesterol in 28 cohort studies
Imputations of missing values in practice: Results from imputations of serum cholesterol in 28 cohort studies
复制标题
DOI：
10.1093/aje/kwh175
复制
发表时间：
2004-07-01
影响因子：
5
通讯作者：
Woodward, M
中科院分区：
文献类型：
--
作者：
Barzi, F;Woodward, M
流行病学研究中常见的缺失值是获得有效估计值的主要问题。模拟研究表明，多重填补是填补缺失值的一种有吸引力的方法，但它相对复杂，需要专门的软件。对于亚太队列研究协作组的28项研究中的每一项研究，本文比较了8种插补程序（无条件和条件平均值、多个热甲板、期望最大化和4种不同的多重插补方法）和朴素的完整参与者分析。用于比较的标准是总胆固醇的平均值和标准差，以及胆固醇增加一个单位的估计冠状动脉死亡风险比。进一步的敏感性分析允许系统性高估或低估胆固醇。对于22项胆固醇值缺失率低于10%的研究，以及汇总的亚太队列研究协作组，所有方法得出的结果相似。对于大约10-60%缺失值的研究，方法之间存在明显差异，在这种情况下，过去的研究表明多重插补是首选方法。对于缺失值超过60%的两项研究，似乎没有填补方法令人满意。
Missing values, common in epidemiologic studies, are a major issue in obtaining valid estimates. Simulation studies have suggested that multiple imputation is an attractive method for imputing missing values, but it is relatively complex and requires specialized software. For each of 28 studies in the Asia Pacific Cohort Studies Collaboration, a comparison of eight imputation procedures (unconditional and conditional mean, multiple hot deck, expectation maximization, and four different approaches to multiple imputation) and the naive, complete participant analysis are presented in this paper. Criteria used for comparison were the mean and standard deviation of total cholesterol and the estimated coronary mortality hazard ratio for a one-unit increase in cholesterol. Further sensitivity analyses allowed for systematic over- or underestimation of cholesterol. For 22 studies for which less than 10% of the values for cholesterol were missing, and for the pooled Asia Pacific Cohort Studies Collaboration, all methods gave similar results. For studies with roughly 10-60% missing values, clear differences existed between the methods, in which case past research suggests that multiple imputation is the method of choice. For two studies with over 60% missing values, no imputation method seemed to be satisfactory.