R Companion to Epidemiology: Study Design and Data Analysis
eBook - ePub

R Companion to Epidemiology: Study Design and Data Analysis

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

R Companion to Epidemiology: Study Design and Data Analysis

About this book

R Companion to Epidemiology: Study Design and Data Analysis is a companion volume to the classic textbook by Mark Woodward, Epidemiology: Study Design and Data Analysis, Third Edition. It aims to equip the reader with sufficient knowledge to use R for practising epidemiology. Towards this aim, it reworks the examples in the textbook, presenting the code followed by an explanation and its result.

Key Features:

  • Almost all of the numerical examples in the textbook are reworked in R
  • R code is introduced in small portions and explained thoroughly
  • Complexity of introduced code is increased only gradually
  • More than 300 commands spanning more than 40 libraries are introduced

The book is intended primarily to be used as a supplement to the textbook by undergraduate and graduate students in the fields of epidemiology and statistics. It will also serve practitioners and researchers in epidemiology who want to learn R for use in their work.

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Yes, you can access R Companion to Epidemiology: Study Design and Data Analysis by Ajith R in PDF and/or ePUB format, as well as other popular books in Mathematics & Biostatistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
CRC Press
Year
2025
eBook ISBN
9781040355015
Edition
0

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Title Page
  4. Copyright Page
  5. Dedication Page
  6. Contents
  7. List of Figures
  8. List of Tables
  9. Foreword
  10. Preface
  11. Preliminaries
  12. 1 Fundamental issues
  13. 2 Basic analytical procedures
  14. 3 Assessing risk factors
  15. 4 Confounding and interaction
  16. 5 Cohort studies
  17. 6 Case-control studies
  18. 7 Intervention studies
  19. 8 Sample size determination
  20. 9 Modelling quantitative outcome variables
  21. 10 Modelling binary outcome data
  22. 11 Modelling follow-up data
  23. 12 Meta-analysis
  24. 13 Risk scores and clinical decision rules
  25. 14 Computer-intensive methods
  26. Final Words
  27. Command Index
  28. Subject Index