title: Advanced statistics: missing data in clinical research--part 2: multiple imputation.
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.
authors.affiliation: Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, OR, USA. newgardc@ohsu.edu
authors.forename: Craig D
authors.initials: CD
authors.lastname: Newgard
authors.forename: Jason S
authors.initials: JS
authors.lastname: Haukoos
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data; D035843:Biomedical Research; D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models; D012107:Research Design; D012984:Software; D013223:Statistics as Topic