He Yulei, Zhang Guangyu
Division of Research and Methodology, U.S. Centers for Desease Control and Prevention.
National Center for Health Statistics, U.S. Centers for Desease Control and Prevention.
Surv Stat. 2023 Jan;87:37-47.
Multiple imputation (MI) is a widely used analytic approach to address missing data problems. SAS (SAS Institute Inc, Cary, N.C.) has established MI procedures including PROC MI and PROC MIANALYZE. We illustrate the use of these procedures for conducting MI analysis of complex survey data by an example from RANDS. Section 1 contains the introduction. Section 2 includes some necessary methodological background. Section 3 shows a MI example with an arbitrary missing data pattern. Section 4 concludes the paper with a discussion.
多重填补（MI）是一种广泛应用于解决数据缺失问题的分析方法。SAS（SAS软件研究所，北卡罗来纳州卡里）已建立了包括PROC MI和PROC MIANALYZE在内的多重填补程序。我们通过兰德公司（RANDS）的一个示例来说明如何使用这些程序对复杂的调查数据进行多重填补分析。第1节包含引言。第2节介绍一些必要的方法学背景。第3节展示一个具有任意缺失数据模式的多重填补示例。第4节通过讨论总结本文。
