This book provides a modern, hands-on guide to the essential
concepts and ideas for analyzing data with missing observations in
the field of biostatistics. It acknowledges the limitations of
established techniques and provides concrete applications of newly
developed methods. It covers traditional techniques for missing
data inference including likelihood-based, weighted GEE, multiple
imputation, and Bayesian methods and applies the methodology to
rapidly developing areas of research. The book is ideal for courses
on biostatistics at the upper-undergraduate and graduate levels and
for health science researchers and applied statisticians.
General
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