Abstract
Missing data frequently arise in the course of research studies. Understanding the mechanism that led to the missing data is important in order for investigators to be able to perform analyses that will lead to proper inference. This chapter will review different missing data mechanisms, including random and non-random mechanisms. Basic methods will be presented using examples to illustrate approaches to analyzing data in the presence of missing data.
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© 2007 Humana Press Inc., Totowa, NJ
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D’Agostino, R.B. (2007). Overview of Missing Data Techniques. In: Ambrosius, W.T. (eds) Topics in Biostatistics. Methods in Molecular Biology™, vol 404. Humana Press. https://doi.org/10.1007/978-1-59745-530-5_17
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DOI: https://doi.org/10.1007/978-1-59745-530-5_17
Publisher Name: Humana Press
Print ISBN: 978-1-58829-531-6
Online ISBN: 978-1-59745-530-5
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