R Companion to Epidemiology: Study Design and Data Analysis

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.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.

Autorentext

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.

Klappentext

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.

Inhalt

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.