Statistics for Health Data Science: An Organic Approach
暫譯: 健康數據科學的統計學：有機方法

Etzioni, Ruth, Mandel, Micha, Gulati, Roman

Statistics for Health Data Science: An Organic Approach

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商品描述

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/

商品描述(中文翻譯)

學生和健康科學研究者面臨前所未有的機會與挑戰。這些機會來自於公開數據的爆炸性增長，這些數據同時啟發並引導新的研究方向。挑戰在於，所需的分析工具遠超過基本統計的標準方法和模型。本教科書旨在為健康照護研究者提供現代健康分析工具包中最重要的元素，並結合統計學、健康計量經濟學和數據科學的領域。

本教科書旨在克服學生對數據和統計的焦慮，幫助他們成為健康照護研究中適當分析方法的自信使用者。方法以有機的方式呈現，新材料自然地建立在之前的內容之上。每種技術都以一個當前的研究問題為動機，用非技術性術語解釋，並附有引人入勝的解釋和範例。這樣，作者培養了對各種分析技術的深刻（「有機」）理解，包括它們的假設和數據需求，以及它們的優勢和限制。他們通過分析來自各種公開數據庫的真實數據來說明所有課程，針對相關的研究問題，並將結果與已發表的研究進行比較。最終，本教科書旨在培養對健康數據科學有深思熟慮且知識淵博的健康服務研究者，而非僅僅是數據分析師。

本教科書與競爭對手的不同之處在於其獨特的方法組合，以及確保讀者理解何時、如何及為何應用這些方法的決心。它為公共衛生研究者提供了一種分析科學問題的思維方式，並提供了將數據與有效分析方法配對的良好指導。讀者應該感到有勇氣使用傳統統計模型、健康計量經濟學方法，甚至預測算法來處理真實公共數據集的分析。

隨書提供的代碼和數據集可在作者網站獲得：https://roman-gulati.github.io/statistics-for-health-data-science/

作者簡介

Ruth Etzioni, PhD has been on the faculty at the Fred Hutchinson Cancer Research Center since 1991 and is an affiliate professor of biostatistics and health services at the University of Washington. She develops statistical models and methods for health policy and is a member of national cancer policy panels including the American Cancer Society and the National Comprehensive Cancer Network. She has developed and taught a new curriculum in statistical methods for graduate students in the School of Public Health at the University of Washington; the course focuses on health care analytics using contemporary, publicly available data resources. The popularity of this course led her to conceive of and develop the proposed text. Dr. Etzioni received her undergraduate degree in Computer Science and Operations Research from the University of Cape Town and her PhD in Statistics from Carnegie-Mellon University.

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 statistical models to investigate population impacts of national clinical practice patterns and cancer control policies. He has led or contributed to many independent and collaborative modeling studies for the Cancer Intervention and Surveillance Modeling Network of the National Cancer Institute. He is also chief biostatistician for the prostate cancer research program at the Fred Hutch and the University of Washington, supporting many molecular, preclinical, and clinical research studies. Mr. Gulati received graduate training first in mathematics and then in Chinese before earning his MS in Statistics from Oregon State University.

作者簡介(中文翻譯)

露絲·艾齊奧尼 (Ruth Etzioni), PhD 自1991年以來一直在弗雷德·哈欽森癌症研究中心任教，並且是華盛頓大學生物統計學和健康服務的兼任教授。她為健康政策開發統計模型和方法，並且是美國癌症學會和全國綜合癌症網絡等國家癌症政策小組的成員。她在華盛頓大學公共衛生學院開發並教授了一門新的統計方法課程，該課程專注於使用當代的公共數據資源進行醫療保健分析。這門課程的受歡迎程度使她構思並開發了這本提議的書籍。艾齊奧尼博士在開普敦大學獲得計算機科學和運籌學的學士學位，並在卡內基梅隆大學獲得統計學的博士學位。

米哈·曼德爾 (Micha Mandel), PhD 是耶路撒冷希伯來大學的統計學教授。米哈在本科生到博士生的各個層級都有豐富的教學經驗，並且參與了醫學和醫療保健領域的各種問題。他與來自不同領域的學生和研究人員的互動使他開發了工具，以便以易於理解的方式解釋複雜的統計概念和方法。他的主要研究領域包括偏倚抽樣、生存分析和法醫統計，但他持續擴展他的研究範疇，最近的研究集中在COVID-19自然歷史的估計上。他在許多高知名度的統計期刊上發表過文章，包括Biometrics、Biometrika、Journal of the American Statistical Association和Statistics in Medicine。米哈在耶路撒冷希伯來大學獲得統計學的博士學位。

羅曼·古拉提 (Roman Gulati), MS 自2005年以來一直是弗雷德·哈欽森癌症研究中心的高級統計分析師。古拉提先生是設計、開發和分析統計模型的專家，旨在調查國家臨床實踐模式和癌症控制政策對人口的影響。他曾主導或參與許多獨立和合作的建模研究，這些研究是全國癌症研究所的癌症干預和監測建模網絡的一部分。他還是弗雷德·哈欽森和華盛頓大學前列腺癌研究計劃的首席生物統計學家，支持許多分子、臨床前和臨床研究。古拉提先生最初在數學和中文方面接受研究生培訓，然後在俄勒岡州立大學獲得統計學的碩士學位。

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