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Statistics for Health Data Science An Organic Approach Springer Texts in Statistics

by [Etzioni, Ruth, Mandel, Micha, Gulati, Roman]

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Last update: 2025-04-16 20:01:35.419

Description

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

Last updated on 2025-04-16 20:01:35.419

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