Methods for handling missing data in clinical psychology studies are reviewed. Missing data are defined, and a taxonomy of main approaches to analysis is presented, including complete-case and available-case analysis, weighting, maximum likelihood, Bayes, single and multiple imputation, and augmented inverse probability weighting. Missingness mechanisms, which play a key role in the performance of alternative methods, are defined. Approaches to robust inference, and to inference when the mechanism is potentially missing not at random, are discussed.Expected final online publication date for the Annual Review of Clinical Psychology, Volume 20 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

中文翻译：

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 缺失数据分析


回顾了临床心理学研究中处理缺失数据的方法。定义了缺失数据，并提出了主要分析方法的分类，包括完整案例和可用案例分析、加权、最大似然、贝叶斯、单一和多重插补以及增强逆概率加权。定义了在替代方法的性能中发挥关键作用的缺失机制。讨论了稳健推理的方法，以及机制可能非随机缺失时的推理。《临床心理学年度评论》第 20 卷的预计最终在线发布日期为 2024 年 5 月。请参阅 http://www.annualreviews。 org/page/journal/pubdates 了解修订后的估计。