Newgard Craig D, Haukoos Jason S
Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, OR, USA.
Acad Emerg Med. 2007 Jul;14(7):669-78. doi: 10.1197/j.aem.2006.11.038.
In part 1 of this series, the authors describe the importance of incomplete data in clinical research, and provide a conceptual framework for handling incomplete data by describing typical mechanisms and patterns of censoring, and detailing a variety of relatively simple methods and their limitations. In part 2, the authors will explore multiple imputation (MI), a more sophisticated and valid method for handling incomplete data in clinical research. This article will provide a detailed conceptual framework for MI, comparative examples of MI versus naive methods for handling incomplete data (and how different methods may impact subsequent study results), plus a practical user's guide to implementing MI, including sample statistical software MI code and a deidentified precoded database for use with the sample code.
在本系列的第1部分中，作者阐述了临床研究中不完整数据的重要性，并通过描述典型的删失机制和模式，以及详细介绍各种相对简单的方法及其局限性，提供了一个处理不完整数据的概念框架。在第2部分中，作者将探讨多重填补（MI），这是一种在临床研究中处理不完整数据的更复杂且有效的方法。本文将为多重填补提供详细的概念框架，给出多重填补与处理不完整数据的简单方法的对比示例（以及不同方法可能如何影响后续研究结果），外加一份实施多重填补的实用用户指南，包括示例统计软件的多重填补代码以及与示例代码配套使用的去识别预编码数据库。
