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R Companion to Epidemiology: Study Design and Data Analysis [Kietas viršelis]

  • Formatas: Hardback, 392 pages, aukštis x plotis: 254x178 mm, weight: 930 g, 56 Tables, black and white; 97 Line drawings, black and white; 97 Illustrations, black and white
  • Išleidimo metai: 23-Jun-2025
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1032964650
  • ISBN-13: 9781032964652
Kitos knygos pagal šią temą:
R Companion to Epidemiology: Study Design and Data AnalysisDidesnis paveikslėlis
  • Formatas: Hardback, 392 pages, aukštis x plotis: 254x178 mm, weight: 930 g, 56 Tables, black and white; 97 Line drawings, black and white; 97 Illustrations, black and white
  • Išleidimo metai: 23-Jun-2025
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1032964650
  • ISBN-13: 9781032964652
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781032964652.html
Raktažodžiai:

A companion book for Epidemiology study design and data analysis 3rd edition. Aims to equip with sufficient knowledge to use R for practising epidemiology. Reworks the examples in epidemiology study design and data analysis using R, presenting the code followed by an explanation and its result.



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.

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.

1. Fundamental Issues.
2. Basic Analytical Procedures.
3. Assessing Risk Factors.
4. Confounding And Interaction.
5. Cohort Studies.
6. Case-Control Studies.
7. Intervention Studies.
8. Sample Size Determination.
9. Modeling Quantitative Outcome Variables.
10. Modeling Binary Outcome Data.
11. Modeling Follow-Up Data.
12. Meta-Analysis.
13. Risk Scores And Clinical Decision Rules.
14. Computer-Intensive Methods.
Dr Ajith R worked as a primary care physician for 21 years after completing graduation. He has a postgraduate diploma in clinical pathology and has completed India Epidemic Intelligence Service Training.