Missing-data methods for generalized linear models: A comparative review

• 成果类型: Review
• 署名作者: Ibrahim, JG; Chen, MH; Lipsitz, SR; Herring, AH
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of Connecticut; Medical University of South Carolina
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000001844
• 发表日期: 2005
• 页码: 332-346
• 关键词: maximum-likelihood-estimation; local influence approach; cure rate models; incomplete-data; semiparametric regression; covariate data; data mechanism; parameter-estimation; contingency-tables; repeated outcomes

摘要

Missing data is a major issue in many applied problems, especially in the biomedical sciences. We review four common approaches for inference in generalized linear models (GLMs) with missing covariate data: maximum likelihood (ML), multiple imputation (MI), fully Bayesian (FB), and weighted estimating equations (WEEs). There is considerable interest in how these four methodologies are related, the properties of each approach, the advantages and disadvantages of each methodology, and computational implementation. We examine data that are missing at random and nonignorable missing. For ML we focus on techniques using the EM algorithm, and in particular, discuss the EM by the method of weights and related procedures as discussed by Ibrahim. For MI, we examine the techniques developed by Rubin. For FB, we review approaches considered by Ibrahim et al. For WEE, we focus on the techniques developed by Robins et al. We use a real dataset and a detailed simulation study to compare the four methods.