Epidemiology with R / Bendix Carstensen.

Format:

Book

Author/Creator:

Carstensen, Bendix, author.

Language:

English

Subjects (All):

R (Computer program language).

Epidemiology--Statistical methods.

Epidemiology.

Physical Description:

1 online resource (246 pages)

Place of Publication:

Oxford, England : Oxford University Press, [2021]

Summary:

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.

Contents:

Cover

Epidemiology with R

Copyright

Contents

Preface

What this book is not

Acknowledgements

List of Figures

Introduction

What you should do

Code chunks

Graphs in this book

Practicing R

Chapter 1: Using R

1.1 Installing and using R

1.2 Documenting your code and results

1.2.1 R markdown

1.2.2 Sweave / knitr

1.2.3 Coding style in R

1.2.4 R lingo

1.3 Simple usage of R

1.3.1 Using R as a calculator

1.3.2 A functional language

Probability functions

Objects and functions

What makes R different: functions

1.3.3 Sequences

1.3.4 The births data

1.3.5 Referencing parts of a data frame

1.3.6 Summaries

1.3.7 Generating new variables

1.3.8 Logical variables

1.3.9 Turning a variable into a factor

Manipulating factor levels

Grouping values of a quantitative variable

1.3.10 Tables

Tables of means and other things

1.3.11 Reading data

1.3.12 Saving data

Saving the work space

Saving R objects in a file

1.3.13 The search path

Attaching a data frame

Using with

1.4 Graphics

1.4.1 ggplot2

1.4.2 Base graphics

1.4.3 Simple base graphs

Plot on the screen

Colours

Adding to a plot

Using indexing for plot elements

Interacting with a plot

Saving graphs for use in other documents

Same graph on multiple devices

The par() command

1.5 Frequency data

1.5.1 Graphical overview

1.5.2 Ad hoc analyses of admissions

1.6 Tables and arrays for results

1.7 Dates in R

1.8 Numerical accuracy

1.8.1 Accuracy of matching variables

1.9 tidyverse and data.table

Chapter 2: Measures of disease occurrence

2.1 Prevalence

2.2 Mortality rate

2.3 Incidence rate

2.4 Standardized mortality ratio

2.5 Survival

2.5.1 Cumulative risk

2.5.2 Competing risks

2.5.3 Sojourn time.

Chapter 3: Prevalence data-models, likelihood, and binomial regression

3.1 Likelihood

3.1.1 A single probability

3.1.2 Simple confidence interval

3.1.3 Confidence intervals in general

3.1.4 The normal distribution

3.1.5 Simple confidence intervals from models

3.1.6 Tests and p-values

3.2 Prevalence by age

3.3 Comparing different models for the same data

3.3.1 Likelihood-ratio test

3.3.2 Deviance

3.3.3 Deviance and goodness of fit

3.3.4 AIC and BIC

Chapter 4: Regression models

4.1 Types of models

4.2 Normal linear regression model

4.3 Simple linear regression

4.4 Multiple regression

4.4.1 Estimation in the normal linear regression model

4.4.2 R-squared

4.4.3 Multiple regression

4.4.4 Standardized variables

4.4.5 Predictions from the normal regression model

4.5 Model formulae in R

4.6 Regression models and generalized linear models

4.6.1 Categorical effects

4.6.2 Linear and categorical effects

4.6.3 ANOVA-ANCOVA

4.6.4 Categorical-linear interaction

Special interaction?

4.6.5 Categorical by categorical interaction

4.7 Collinearity and aliasing

4.8 Logarithmic transformations

4.8.1 Logarithms

4.8.2 Log transform of the response variable

4.8.3 Coefficient of variation

4.8.4 Log transform of an explanatory variable

4.8.5 Log transform of both the response and explanatory variables

Chapter 5: Analysis of follow-up data

5.1 Basic data structure

5.2 Probability model

5.2.1 Data

5.2.2 Likelihood for a rate

5.2.3 Estimates of rates and rate ratios

5.3 Representation of follow-up data

5.3.1 Lexis object for follow-up data

Scaling of Lexis diagrams

5.4 Splitting the follow-up time along a time-scale

5.5 Smooth age-effects for rates

5.5.1 Disaggregated data

5.5.2 Including sex in the model

5.6 SMR.

5.6.1 Modelling the SMR

5.7 Time-dependent variables

5.7.1 Cutting time at a specific date

The precursor states

5.7.2 Modelling time-dependent variables

Survival?

5.7.3 Clinical measurements in cohort studies

Analysis using clinical measurements

Chapter 6: Parametrization and prediction of rates

6.1 Predictions and contrasts

6.2 Prediction of a single rate

6.3 Categorical variables

6.3.1 Groups and rate ratios

Comparing all groups

6.4 Modelling the effect of quantitative variables

6.4.1 Categorizing quantitative variables: don't

6.4.2 Linear effect

Predicting the rates

6.4.3 Polynomial effects

6.4.4 Other types of non-linear effects

Natural splines

Penalized splines

6.5 Two quantitative predictors

6.5.1 Age and period

6.5.2 Age and cohort

6.5.3 Contours of joint effects

Image plot / heatmap

6.6 Quantitative interactions

6.6.1 Age-period interaction

Age-specific rates at different dates (periods)

Period-specific rates at different ages

6.6.2 Age and cohort interaction

6.6.3 Parametric interaction models

6.6.4 Varying coefficients models for interaction

6.6.5 Summary of quantitative interactions

Chapter 7: Case-control and case-cohort studies

7.1 Follow-up and case-control studies

7.1.1 Probabilities and odds in case-control studies

7.1.2 The sampling fractions

7.1.3 A simple example

7.2 Statistical model for the odds ratio

7.2.1 Analysis by logistic regression

7.3 Odds ratio and rate ratio

7.3.1 Incidence density sampling

7.4 Confounding and stratified sampling

7.4.1 Stratified sampling

7.5 Individually matched studies

7.5.1 An example

7.5.2 When conditional analysis is not needed

7.6 Nested case-control studies

7.6.1 Register-based case-control studies

7.7 Case-cohort studies.

Chapter 8: Survival analysis

8.1 Introduction

8.2 Life table estimator of survival function

8.3 Kaplan

Meier estimator of survival

8.3.1 Survival in two groups

8.4 The Cox model

8.4.1 Mean survival or survival at mean?

8.5 The time-scale

8.6 Relation between Cox and Poisson models

8.6.1 Simple parametric mortality functions

Baseline mortality rate

Survival curves

8.6.2 Proportional hazards?

8.6.3 The Cox model as a Poisson model

8.7 Time-dependent covariates

8.8 Competing risks

8.9 Modelling cause specific rates

8.9.1 Limitations

8.10 The Fine

Gray approach to competing risks

8.11 Time-dependent variables and competing risks

Chapter 9: Do not group quantitative variables

9.1 Problems Caused by Categorizing Continuous Variables

References

Index.

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

0-19-257839-1

0-19-187693-3

9780198841326

9780192578396

OCLC:

1233072085