Missing data methods in longitudinal studies: a review

作     者：Ibrahim, Joseph G. Molenberghs, Geert 

作者机构：Univ N Carolina Dept Biostat Chapel Hill NC 27515 USA Hasselt Univ Ctr Stat Int Inst Biostat & Stat Bioinformat B-3590 Diepenbeek Belgium Katholieke Univ Leuven B-3590 Diepenbeek Belgium 

出 版 物：《TEST》 (验算：西班牙统计与运筹学会杂志)

年 卷 期：2009年第18卷第1期

页      面：1-43页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：NCI NIH HHS [R01 CA074015-11A1  R01 CA074015] Funding Source: Medline 

主　　题：Expectation-maximization algorithm Incomplete data Missing completely at random Missing at random Missing not at random Pattern-mixture model Selection model Sensitivity analyses Shared-parameter model 

摘      要：Incomplete data are quite common in biomedical and other types of research, especially in longitudinal studies. During the last three decades, a vast amount of work has been done in the area. This has led, on the one hand, to a rich taxonomy of missing-data concepts, issues, and methods and, on the other hand, to a variety of data-analytic tools. Elements of taxonomy include: missing data patterns, mechanisms, and modeling frameworks;inferential paradigms;and sensitivity analysis frameworks. These are described in detail. A variety of concrete modeling devices is presented. To make matters concrete, two case studies are considered. The first one concerns quality of life among breast cancer patients, while the second one examines data from the Muscatine children s obesity study.