Missing Data Analysis.

IF 12.1 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Annual review of biochemistry Pub Date : 2024-07-01 Epub Date: 2024-07-02 DOI:10.1146/annurev-clinpsy-080822-051727
Roderick J Little
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引用次数: 0

Abstract

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.

缺失数据分析。
本文回顾了临床心理学研究中处理缺失数据的方法。对缺失数据进行了定义,并对主要分析方法进行了分类,包括完整病例和可用病例分析、加权、最大似然法、贝叶斯法、单项和多项估算以及增强反概率加权法。定义了在替代方法的性能中起关键作用的缺失机制。此外,还讨论了稳健推断方法,以及当机制可能不是随机缺失时的推断方法。临床心理学年度评论》第 20 卷的最终在线出版日期预计为 2024 年 5 月。修订后的预计日期请参见 http://www.annualreviews.org/page/journal/pubdates。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annual review of biochemistry
Annual review of biochemistry 生物-生化与分子生物学
CiteScore
33.90
自引率
0.00%
发文量
31
期刊介绍: The Annual Review of Biochemistry, in publication since 1932, sets the standard for review articles in biological chemistry and molecular biology. Since its inception, these volumes have served as an indispensable resource for both the practicing biochemist and students of biochemistry.
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