@article{ref1,
title="Advanced statistics: missing data in clinical research--part 2: multiple imputation",
journal="Academic emergency medicine",
year="2007",
author="Newgard, Craig D. and Haukoos, Jason S.",
volume="14",
number="7",
pages="669-678",
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.<p /><p>Language: en</p>",
language="en",
issn="1069-6563",
doi="10.1197/j.aem.2006.11.038",
url="http://dx.doi.org/10.1197/j.aem.2006.11.038"
}