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Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues
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Statistics for Health Data Science - An Organic Approach (Hardcover, 1st ed. 2020)
Loot Price: R2,668
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Statistics for Health Data Science - An Organic Approach (Hardcover, 1st ed. 2020)
Series: Springer Texts in Statistics
Expected to ship within 18 - 22 working days
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Students and researchers in the health sciences are faced with
greater opportunity and challenge than ever before. The opportunity
stems from the explosion in publicly available data that
simultaneously informs and inspires new avenues of investigation.
The challenge is that the analytic tools required go far beyond the
standard methods and models of basic statistics. This textbook aims
to equip health care researchers with the most important elements
of a modern health analytics toolkit, drawing from the fields of
statistics, health econometrics, and data science. This textbook is
designed to overcome students' anxiety about data and statistics
and to help them to become confident users of appropriate analytic
methods for health care research studies. Methods are presented
organically, with new material building naturally on what has come
before. Each technique is motivated by a topical research question,
explained in non-technical terms, and accompanied by engaging
explanations and examples. In this way, the authors cultivate a
deep ("organic") understanding of a range of analytic techniques,
their assumptions and data requirements, and their advantages and
limitations. They illustrate all lessons via analyses of real data
from a variety of publicly available databases, addressing relevant
research questions and comparing findings to those of published
studies. Ultimately, this textbook is designed to cultivate health
services researchers that are thoughtful and well informed about
health data science, rather than data analysts. This textbook
differs from the competition in its unique blend of methods and its
determination to ensure that readers gain an understanding of how,
when, and why to apply them. It provides the public health
researcher with a way to think analytically about scientific
questions, and it offers well-founded guidance for pairing data
with methods for valid analysis. Readers should feel emboldened to
tackle analysis of real public datasets using traditional
statistical models, health econometrics methods, and even
predictive algorithms. Accompanying code and data sets are provided
in an author site:
https://roman-gulati.github.io/statistics-for-health-data-science/
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