Statistics for Health Data Science
An Organic Approach
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Beschreibung
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/
Highly interdisciplinary - drawing from statistics, health services, economics, and informaticsGoes beyond the formulas, explaining why different methods work, how to choose from among them, and how to avoid misinterpreting results - to create confident users of appropriate analytic methodsAddresses topical questions such as data science versus statistics, prediction versus explanationProvides a wide range of analytic and regression-type models specific to research questions about health care use and costs of careIn-depth discussion on selection bias in observational data methods for inferring causalitySupplementary Material Includes: Code and data for all examples and model analyses, Code for data processing and analysis, Code segments for simulation models
Highly interdisciplinary - drawing from statistics, health services, economics, and informatics Goes beyond the formulas, explaining why different methods work, how to choose from among them, and how to avoid misinterpreting results - to create confident users of appropriate analytic methods Addresses topical questions such as data science versus statistics, prediction versus explanation Provides a wide range of analytic and regression-type models specific to research questions about health care use and costs of care In-depth discussion on selection bias in observational data methods for inferring causality Supplementary Material Includes: Code and data for all examples and model analyses, Code for data processing and analysis, Code segments for simulation models Includes supplementary material: sn.pub/extras
Autor*in
Ruth Etzioni
Themen in »Statistics for Health Data Science«
Analytic Methods Data Science Health Care Databases Health Data Analytics Health Economics Health Outcomes Health Services Machine Learning Medical Claims Medical Claims Data Models Prediction Predictive Modeling Regression Risk