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.

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法，包括完全案例和可用案例分析、加权、最大似然、贝叶斯、单次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起关键作用的缺失机制。讨论了稳健推理的方法，以及当机制可能缺失时进行推理的方法，而不是随机的。