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Statistical Methods In Health Disparity Research Chapman Hallcrc Biostatistics Series 1st Edition J Sunil Rao

  • SKU: BELL-50279436
Statistical Methods In Health Disparity Research Chapman Hallcrc Biostatistics Series 1st Edition J Sunil Rao
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Statistical Methods In Health Disparity Research Chapman Hallcrc Biostatistics Series 1st Edition J Sunil Rao instant download after payment.

Publisher: Chapman and Hall/CRC
File Extension: PDF
File size: 99.95 MB
Pages: 280
Author: J. Sunil Rao
ISBN: 9780367635121, 9780367635169, 9782022061986, 2022061985, 0367635127, 036763516X, 2022061986
Language: English
Year: 2023
Edition: 1

Product desciption

Statistical Methods In Health Disparity Research Chapman Hallcrc Biostatistics Series 1st Edition J Sunil Rao by J. Sunil Rao 9780367635121, 9780367635169, 9782022061986, 2022061985, 0367635127, 036763516X, 2022061986 instant download after payment.

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.

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