Graphical and numerical diagnostic tools to assess suitability of multiple imputations and imputation models
Graphical and numerical diagnostic tools to assess suitability of multiple imputations and imputation models
复制标题
DOI：
10.1002/sim.6926
复制
发表时间：
2016-07-30
影响因子：
2
通讯作者：
Raghunathan, Trivellore
中科院分区：
文献类型：
--
作者：
Bondarenko, Irina;Raghunathan, Trivellore
多重插补已成为分析不完全数据的常用方法。许多软件包可用于对缺失值进行多重插补，并分析由此产生的完整数据集。然而，检查插补有效性的诊断工具是有限的，目前可用的大多数方法需要相当多的知识的插补模型。然而，在许多实际环境中，估算者和分析师可能是不同的个人或来自不同的组织，分析师模型可能与估算者使用的模型一致，也可能不一致。本文开发和评估了一套图形和数值诊断工具，用于两个实际目的：（i）分析师在实际不知道插补模型假设的情况下确定他/她的模型假设下的插补是否合理;（ii）插补者通过检查观察值和插补值的关键特征来微调插补模型。这些工具基于观察值和插补值分布的数值和图形比较，以响应倾向为条件。该方法说明使用模拟数据集下创建的各种情况下。这些例子集中在连续和二进制变量上，但这些原则可以用于扩展其他类型变量的方法。版权所有（c）2016约翰威利父子有限公司
Multiple imputation has become a popular approach for analyzing incomplete data. Many software packages are available to multiply impute the missing values and to analyze the resulting completed data sets. However, diagnostic tools to check the validity of the imputations are limited, and the majority of the currently available methods need considerable knowledge of the imputation model. In many practical settings, however, the imputer and the analyst may be different individuals or from different organizations, and the analyst model may or may not be congenial to the model used by the imputer. This article develops and evaluates a set of graphical and numerical diagnostic tools for two practical purposes: (i) for an analyst to determine whether the imputations are reasonable under his/her model assumptions without actually knowing the imputation model assumptions; and (ii) for an imputer to fine tune the imputation model by checking the key characteristics of the observed and imputed values. The tools are based on the numerical and graphical comparisons of the distributions of the observed and imputed values conditional on the propensity of response. The methodology is illustrated using simulated data sets created under a variety of scenarios. The examples focus on continuous and binary variables, but the principles can be used to extend methods for other types of variables. Copyright (c) 2016 John Wiley & Sons, Ltd.