Practical Healthcare Statistics with Examples in Python and R: A Guide for the Uninitiated
暫譯: 實用醫療統計：Python 和 R 的範例指南

Korvink, Michael

Practical Healthcare Statistics with Examples in Python and R: A Guide for the Uninitiated

相關主題

商品描述

Practical Healthcare Statistics with Examples in Python and R provides a clear and straightforward introduction to statistical methods in healthcare. Designed for recent graduates, new analysts, and professionals transitioning into healthcare analytics, it offers practical guidance on tackling real-world problems using statistical concepts and programming.

The book is divided into three primary sections. The first section provides an introduction to healthcare data and measures. In these chapters, readers will learn about the nuances of administrative claims and electronic health records, as well as common industry measures related to quality and efficiency of care. The second section will cover foundational techniques, such as hypothesis testing and regression analysis, as well as more advanced approaches, like generalized additive models and hierarchical models. In the last section, readers will be introduced to epidemiological techniques such as direct and indirect standardization, measures of disease frequency and association, and time-to-event analysis.

The book emphasizes interpretable methods that are both effective and easy to communicate to clinical and non-technical stakeholders. Each technique presented in the book is accompanied by statistical notation described in plain English, as well as a self-contained example implemented in both Python and R. These examples help readers connect statistical methods to real healthcare scenarios without requiring extensive programming experience. By working through these examples, readers will build technical skills and a practical understanding of how to analyze healthcare data.

These methods are not only central to improving patient care but are also adaptable to other areas within and beyond healthcare. This book is a practical resource for analysts, data scientists, health researchers, and others looking to make informed, data-driven decisions in healthcare.

商品描述(中文翻譯)

《實用醫療統計學：以 Python 和 R 為例》提供了醫療領域統計方法的清晰且直接的介紹。這本書專為剛畢業的學生、新進分析師以及轉職至醫療分析的專業人士設計，提供了針對現實問題的實用指導，幫助讀者運用統計概念和程式設計解決實際問題。

本書分為三個主要部分。第一部分介紹醫療數據和衡量指標。在這些章節中，讀者將學習到行政索賠和電子健康紀錄的細微差別，以及與護理質量和效率相關的常見行業指標。第二部分將涵蓋基礎技術，如假設檢定和迴歸分析，以及更高級的方法，如廣義加法模型和階層模型。在最後一部分，讀者將接觸到流行病學技術，例如直接和間接標準化、疾病頻率和關聯的衡量指標，以及事件發生時間分析。

本書強調可解釋的方法，這些方法既有效又易於向臨床和非技術利益相關者傳達。書中每個技術都附有用簡單英語描述的統計符號，以及在 Python 和 R 中實現的獨立範例。這些範例幫助讀者將統計方法與實際醫療情境聯繫起來，而不需要廣泛的程式設計經驗。通過這些範例的練習，讀者將建立技術技能，並對如何分析醫療數據有實際的理解。

這些方法不僅對改善病人護理至關重要，還可以適用於醫療內部及其他領域。本書是分析師、數據科學家、健康研究人員及其他希望在醫療領域做出明智、數據驅動決策的人的實用資源。

作者簡介

Michael Korvink serves as Principal, Thought Leadership at Premier, Inc. and is a member of the graduate teaching faculty in the Public Health Sciences department at the University of North Carolina (UNC) at Charlotte. In his current role at Premier, Michael is responsible for collaborative research across health systems, academic institutions, and government agencies. Michael has over 20 years of experience in the healthcare and pharmaceutical industry, and publishes regularly on research methods related to quality, safety, and efficiency of care. Michael holds a Master of Arts from UNC Charlotte, is a Professional Accredited Statistician (PStat) through the American Statistical Association, and is pursuing a doctorate in public health at the Medical College of Wisconsin's Institute for Health and Equity.

作者簡介(中文翻譯)

邁克爾·科爾文克擔任 Premier, Inc. 的首席思想領導者，並且是北卡羅來納大學夏洛特分校公共衛生科學系的研究生教學成員。在 Premier 的目前職位上，邁克爾負責在健康系統、學術機構和政府機構之間進行合作研究。邁克爾在醫療保健和製藥行業擁有超過 20 年的經驗，並定期發表有關質量、安全性和護理效率的研究方法的文章。邁克爾擁有北卡羅來納大學夏洛特分校的文學碩士學位，通過美國統計協會獲得專業認證統計師 (PStat) 資格，並正在威斯康辛醫學院健康與公平研究所攻讀公共衛生博士學位。

類似商品