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Advanced Statistics: Missing Data in Clinical Research—Part 2: Multiple Imputation
Author(s) -
Newgard Craig D.,
Haukoos Jason S.
Publication year - 2007
Publication title -
academic emergency medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.221
H-Index - 124
eISSN - 1553-2712
pISSN - 1069-6563
DOI - 10.1111/j.1553-2712.2007.tb01856.x
Subject(s) - imputation (statistics) , missing data , medicine , censoring (clinical trials) , data mining , software , statistics , computer science , machine learning , pathology , mathematics , programming language
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.