Missing data and multiple imputation in clinical epidemiological research.
Missing data and multiple imputation in clinical epidemiological research.
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DOI：
10.2147/clep.s129785
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发表时间：
2017
影响因子：
3.9
通讯作者：
Petersen I
中科院分区：
文献类型：
--
作者：
Pedersen AB;Mikkelsen EM;Cronin-Fenton D;Kristensen NR;Pham TM;Pedersen L;Petersen I
缺失数据在临床流行病学研究中普遍存在。缺失数据的个体在关注的结局和预后方面可能与无缺失数据的个体不同。缺失数据通常分为以下三种类型：完全随机缺失（MCAR）、随机缺失（MAR）和非随机缺失（MNAR）。在临床流行病学研究中，缺失数据很少是MCAR。数据缺失可能对结果的分析和解释构成相当大的挑战，并可能削弱结果和结论的有效性。已经制定了一些处理缺失数据的方法。这些方法包括完整病例分析、缺失指标法、单值插补以及包含最差情况和最佳情况的敏感性分析。如果在MCAR假设下应用，其中一些方法可以提供无偏但通常不太精确的估计。多重插补是处理缺失数据的一种替代方法，它可以解释与缺失数据相关的不确定性。在MAR假设下，在大多数统计软件中实施多重插补，并基于来自可用数据的信息提供无偏和有效的关联估计。该方法不仅影响有缺失数据的变量的系数估计，而且还影响无缺失数据的其他变量的估计。
Missing data are ubiquitous in clinical epidemiological research. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data are often categorized into the following three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In clinical epidemiological research, missing data are seldom MCAR. Missing data can constitute considerable challenges in the analyses and interpretation of results and can potentially weaken the validity of results and conclusions. A number of methods have been developed for dealing with missing data. These include complete-case analyses, missing indicator method, single value imputation, and sensitivity analyses incorporating worst-case and best-case scenarios. If applied under the MCAR assumption, some of these methods can provide unbiased but often less precise estimates. Multiple imputation is an alternative method to deal with missing data, which accounts for the uncertainty associated with missing data. Multiple imputation is implemented in most statistical software under the MAR assumption and provides unbiased and valid estimates of associations based on information from the available data. The method affects not only the coefficient estimates for variables with missing data but also the estimates for other variables with no missing data.