Statistics for Health Data Science / Springer Texts in Statistics (PDF)
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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 engagingexplanations 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 tackleanalysis 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/
Micha Mandel, PhD, is professor of statistics at the Hebrew University of Jerusalem. Micha has vast experience teaching at all levels from undergraduate to PhD students, and has been engaged with a wide range of problems in medicine and health care. His interaction with students and researchers from different fields led him to develop tools to explain sophisticated statistical concepts and methods in ways that are accessible to many audiences. His main areas of research include biased sampling, survival analysis, and forensic statistics, but he continues to expand his reach, most recently to the estimation of COVID-19 natural history. He has published in many high-profile statistical journals including Biometrics, Biometrika, Journal of the American Statistical Association, and Statistics in Medicine. Micha received his PhD in Statistics from the Hebrew University of Jerusalem.
Roman Gulati, MS, has been a senior statistical analyst at the Fred Hutchinson Cancer Research Center since 2005. Mr. Gulati is a designer, developer, and analyst of
- Autoren: Ruth Etzioni , Micha Mandel , Roman Gulati
- 2021, 1st ed. 2020, 222 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3030598896
- ISBN-13: 9783030598891
- Erscheinungsdatum: 04.01.2021
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- Dateiformat: PDF
- Größe: 5.70 MB
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