A health disparity refers to a higher burden of illness, injury, disability, or mortality experienced by one group relative to others attributable to multiple factors including socioeconomic status, environmental factors, insufficient access to health care, individual risk factors, and behaviors and inequalities in education. These disparities may be due to many factors including age, income, and race. Statistical Methods in Health Disparity Research will focus on their estimation, ranging from classical approaches including the quantification of a disparity, to more formal modeling, to modern approaches involving more flexible computational approaches.

Features:

Presents an overview of methods and applications of health disparity estimation
First book to synthesize research in this field in a unified statistical framework
Covers classical approaches, and builds to more modern computational techniques
Includes many worked examples and case studies using real data
Discusses available software for estimation

The book is designed primarily for researchers and graduate students in biostatistics, data science, and computer science. It will also be useful to many quantitative modelers in genetics, biology, sociology, and epidemiology.

About the Author

J. Sunil Rao, Ph.D. is Professor of Biostatistics in the School of Public Health at the University of Minnesota, Twin Cities and Founding Director Emeritus in the Division of Biostatistics at the Miller School of Medicine, University of Miami.

He has published widely about methods for complex data modeling including high dimensional model selection, mixed model prediction, small area estimation, and bump hunting machine learning, as well as statistical methods for applied cancer biostatistics.

He is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute.

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