Abstract
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.
Original language | English (US) |
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Pages (from-to) | 669-678 |
Number of pages | 10 |
Journal | Academic Emergency Medicine |
Volume | 14 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2007 |
Keywords
- bias
- clinical research
- imputation
- missing data
- multiple imputation
- statistical analysis
ASJC Scopus subject areas
- Emergency Medicine