Haukoos Jason S, Newgard Craig D
Department of Emergency Medicine, Denver Health Medical Center, Denver, CO, USA.
Acad Emerg Med. 2007 Jul;14(7):662-8. doi: 10.1197/j.aem.2006.11.037. Epub 2007 May 30.
Missing data are commonly encountered in clinical research. Unfortunately, they are often neglected or not properly handled during analytic procedures, and this may substantially bias the results of the study, reduce study power, and lead to invalid conclusions. In this two-part series, the authors will introduce key concepts regarding missing data in clinical research, provide a conceptual framework for how to approach missing data in this setting, describe typical mechanisms and patterns of censoring of data and their relationships to specific methods of handling incomplete data, and describe in detail several simple and more complex methods of handling such data. In part 1, the authors will describe relatively simple approaches to handling missing data, including complete-case analysis, available-case analysis, and several forms of single imputation, including mean imputation, regression imputation, hot and cold deck imputation, last observation carried forward, and worst case analysis. In part 2, the authors will describe in detail multiple imputation, a more sophisticated and valid method for handling missing data.
在临床研究中，缺失数据很常见。不幸的是，在分析过程中，它们常常被忽视或处理不当，这可能会严重歪曲研究结果，降低研究效能，并导致无效的结论。在这个分为两部分的系列文章中，作者将介绍临床研究中关于缺失数据的关键概念，提供一个在这种情况下如何处理缺失数据的概念框架，描述数据删失的典型机制和模式以及它们与处理不完整数据的特定方法之间的关系，并详细描述几种处理此类数据的简单和更复杂的方法。在第1部分中，作者将描述处理缺失数据的相对简单的方法，包括完全病例分析、可用病例分析以及几种形式的单一填补法，包括均值填补、回归填补、热卡和冷卡填补、末次观察值结转以及最坏情况分析。在第2部分中，作者将详细描述多重填补法，这是一种处理缺失数据的更复杂且有效的方法。
