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Epidemiology with R [Kõva köide]

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(Steno Diabetes Center, Gentofte and Department of Biostatistics, University of Copenhagen, Denmark)
  • Formaat: Hardback, 256 pages, kõrgus x laius x paksus: 254x195x19 mm, kaal: 708 g
  • Ilmumisaeg: 31-Dec-2020
  • Kirjastus: Oxford University Press
  • ISBN-10: 0198841329
  • ISBN-13: 9780198841326
Teised raamatud teemal:
Epidemiology with RSuurem pilt
  • Formaat: Hardback, 256 pages, kõrgus x laius x paksus: 254x195x19 mm, kaal: 708 g
  • Ilmumisaeg: 31-Dec-2020
  • Kirjastus: Oxford University Press
  • ISBN-10: 0198841329
  • ISBN-13: 9780198841326
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780198841326.html
Märksõnad:
This practical guide is designed for students and researchers with an existing knowledge of R who wish to learn how to apply it in an epidemiological context and exploit its versatility. It also serves as a broader introduction to the quantitative aspects of modern practical epidemiology. The standard tools used in epidemiology are described and the practical use of R for these is clearly explained and laid out. R code examples, many with output, are embedded throughout the text. The entire code is also available on the companion website so that readers can reproduce all the results and graphs featured in the book.

Epidemiology with R is an advanced textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of human and non-human epidemiology, public health, veterinary science, and biostatistics.

Arvustused

This volume will be useful to epidemiologists who are looking for a quick reference guide on how to practically use R in epidemiological research, particularly the Epi and survival packages. * Rose Saint Fleur-Calixte, Quarterly Review of Biology *

Preface ix
Acknowledgements xi
List of Figures
xiii
Introduction 1(2)
1 Using R
3(38)
1.1 Installing and using R
3(1)
1.2 Documenting your code and results
3(3)
1.3 Simple usage of R
6(15)
1.4 Graphics
21(7)
1.5 Frequency data
28(5)
1.6 Tables and arrays for results
33(2)
1.7 Dates in R
35(2)
1.8 Numerical accuracy
37(2)
1.9 tidyverse and data.table
39(2)
2 Measures of disease occurrence
41(6)
2.1 Prevalence
41(1)
2.2 Mortality rate
42(2)
2.3 Incidence rate
44(1)
2.4 Standardized mortality ratio
44(1)
2.5 Survival
45(2)
3 Prevalence data---models, likelihood, and binomial regression
47(18)
3.1 Likelihood
47(8)
3.2 Prevalence by age
55(5)
3.3 Comparing different models for the same data
60(5)
4 Regression models
65(28)
4.1 Types of models
65(1)
4.2 Normal linear regression model
66(1)
4.3 Simple linear regression
67(1)
4.4 Multiple regression
68(6)
4.5 Model formulae in R
74(1)
4.6 Regression models and generalized linear models
75(8)
4.7 Collinearity and aliasing
83(4)
4.8 Logarithmic transformations
87(6)
5 Analysis of follow-up data
93(26)
5.1 Basic data structure
93(1)
5.2 Probability model
94(3)
5.3 Representation of follow-up data
97(4)
5.4 Splitting the follow-up time along a time-scale
101(3)
5.5 Smooth age-effects for rates
104(3)
5.6 SMR
107(4)
5.7 Time-dependent variables
111(8)
6 Parametrization and prediction of rates
119(40)
6.1 Predictions and contrasts
119(1)
6.2 Prediction of a single rate
119(2)
6.3 Categorical variables
121(3)
6.4 Modelling the effect of quantitative variables
124(10)
6.5 Two quantitative predictors
134(11)
6.6 Quantitative interactions
145(14)
7 Case-control and case-cohort studies
159(22)
7.1 Follow-up and case-control studies
160(3)
7.2 Statistical model for the odds ratio
163(3)
7.3 Odds ratio and rate ratio
166(1)
7.4 Confounding and stratified sampling
166(4)
7.5 Individually matched studies
170(4)
7.6 Nested case-control studies
174(3)
7.7 Case-cohort studies
177(4)
8 Survival analysis
181(38)
8.1 Introduction
181(1)
8.2 Life table estimator of survival function
181(3)
8.3 Kaplan-Meier estimator of survival
184(4)
8.4 The Cox model
188(5)
8.5 The time-scale
193(3)
8.6 Relation between Cox and Poisson models
196(10)
8.7 Time-dependent covariates
206(4)
8.8 Competing risks
210(4)
8.9 Modelling cause specific rates
214(3)
8.10 The Fine-Gray approach to competing risks
217(1)
8.11 Time-dependent variables and competing risks
217(2)
9 Do not group quantitative variables
219(4)
9.1 Problems Caused by Categorizing Continuous Variables
219(4)
References 223(2)
Index 225
Bendix Carstensen is a Senior Statistician in Clinical Epidemiology at the Steno Diabetes Center, Gentofte and an External Lecturer at the Department of Biostatistics, University of Copenhagen, Denmark. His expertise and interests are chiefly in the areas of Biostatistics, Epidemiology/Public Health and Diabetes Epidemiology.