Statistical Thinking in Epidemiology (Paperback)
暫譯: 流行病學中的統計思維 (平裝本)

Tu, Yu-Kang, Gilthorpe, Mark

Statistical Thinking in Epidemiology (Paperback)
  • 出版商: CRC
  • 出版日期: 2019-09-19
  • 售價: $3,140
  • 貴賓價: 9.5 折 $2,983
  • 語言: 英文
  • 頁數: 232
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0367382555
  • ISBN-13: 9780367382551
  • 相關分類: 機率統計學 Probability-and-statistics

    • 海外代購書籍(需單獨結帳)

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商品描述

While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research: mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction.

 

 

Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking.

 

 

 

 

Throughout the book, statistical software packages R and Stata are used for general statistical modeling, and Amos and Mplus are used for structural equation modeling.

 

 

商品描述(中文翻譯)

雖然生物醫學研究人員可能能夠遵循隨附於統計軟體包的手冊中的指示，但他們並不總是具備足夠的知識來選擇適當的統計方法並正確解釋其結果。流行病學中的統計思維探討了在醫學和流行病學研究中使用相關性和迴歸時常見的方法論和統計問題：數學耦合、均值回歸、多重共線性、反轉悖論和統計互動。

 

 

流行病學中的統計思維是關於在研究流行病學問題時進行統計思考。作者專注於幾種方法並詳細探討它們：流行病學中的具體例子說明了不同的模型規範如何暗示變數之間不同的因果關係，並在解釋模型時適當考慮隱含或明確的因果關係的背景。本書旨在為應用統計學家和流行病學家而寫，但對於希望更好理解統計思維的臨床和應用健康研究人員也非常有用。

 

 

 

 

在整本書中，使用統計軟體包 R 和 Stata 進行一般統計建模，並使用 Amos 和 Mplus 進行結構方程建模。

 

 

作者簡介

Dr Yu-Kang Tu is a Senior Clinical Research Fellow in the Division of Biostatistics, School of Medicine, and in the Leeds Dental Institute, University of Leeds, Leeds, UK. He was a visiting Associate Professor to the National Taiwan University, Taipei, Taiwan. First trained as a dentist and then an epidemiologist, he has published extensively in dental, medical, epidemiological and statistical journals. He is interested in developing statistical methodologies to solve statistical and methodological problems such as mathematical coupling, regression to the mean, collinearity and the reversal paradox. His current research focuses on applying latent variables methods, e.g. structural equation modeling, latent growth curve modelling, and lifecourse epidemiology. More recently, he has been working on applying partial least squares regression to epidemiological data.

 

Prof Mark S Gilthorpe is professor of Statistical Epidemiology, Division of Biostatistics, School of Medicine, University of Leeds, Leeds, UK. Having completed a single honours degree in mathematical Physics (University of Nottingham), he undertook a PhD in Mathematical Modelling (University of Aston in Birmingham), before initially embarking upon a career as self-employed Systems and Data Analyst and Computer Programmer, and eventually becoming an academic in biomedicine. Academic posts include systems and data analyst of UK regional routine hospital data in the Department of Public Health and Epidemiology, University of Birmingham; Head of Biostatistics at the Eastman Dental Institute, University College London; and founder and Head of the Division of Biostatistics, School of Medicine, University of Leeds. His research focus has persistently been that of the development and promotion of robust and sophisticated modelling methodologies for non-experimental (and sometimes large and complex) observational data within biomedicine, leading to extensive publications in

作者簡介(中文翻譯)

杜宇康博士是英國利茲大學醫學院生物統計學部的高級臨床研究研究員，並在利茲牙科研究所任職。他曾擔任國立台灣大學的訪問副教授。杜博士最初接受牙醫訓練，隨後成為流行病學家，並在牙科、醫學、流行病學和統計學期刊上發表了大量文章。他對開發統計方法學以解決統計和方法論問題（如數學耦合、均值回歸、多重共線性和反轉悖論）感興趣。他目前的研究重點是應用潛在變數方法，例如結構方程模型、潛在成長曲線模型和生命週期流行病學。最近，他一直在研究將偏最小二乘回歸應用於流行病學數據。

馬克·吉爾索普教授是英國利茲大學醫學院生物統計學部的統計流行病學教授。他在諾丁漢大學完成了數學物理的單科榮譽學位，隨後在伯明翰阿斯頓大學攻讀數學建模的博士學位，最初從事自僱系統和數據分析師及計算機程序員的職業，最終成為生物醫學領域的學者。學術職位包括伯明翰大學公共衛生與流行病學系的英國地區常規醫院數據系統和數據分析師；倫敦大學東曼牙科研究所的生物統計學部主任；以及利茲大學醫學院生物統計學部的創始人和主任。他的研究重點始終是開發和推廣針對生物醫學中非實驗性（有時是大型和複雜）觀察數據的穩健和精緻的建模方法，並在此領域發表了大量文章。

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