Biostatistics for Epidemiology and Public Health Using R / Edition 1

by Bertram K.C. Chan PhD

Overview

Provides a comprehensive explanation for data analysis and graphics using R language, including how R language handles classic problems in case-control, cohort studies and its use in survival analysis... The content and quality of this book is excellent. It is a great tool for understanding the use of R language for biostatistical analysis. Score: 91 - 4 Stars!

Bhavesh Barad, MD, East Tennessee State University Quillen College of Medicine, Doody's Reviews

Since it first appeared in 1996, the open-source programming language R has become increasingly popular as an environment for statistical analysis and graphical output. In addition to being freely available, R offers several advantages for biostatistics, including strong graphics capabilities, the ability to write customized functions, and its extensibility. This is the first textbook to present classical biostatistical analysis for epidemiology and related public health sciences to students using the R language. Based on the assumption that readers have minimal familiarity with statistical concepts, the author uses a step-bystep approach to building skills.

The text encompasses biostatistics from basic descriptive and quantitative statistics to survival analysis and missing data analysis in epidemiology. Illustrative examples, including real-life research problems and exercises drawn from such areas as nutrition, environmental health, and behavioral health, engage students and reinforce the understanding of R. These examples illustrate the replication of R for biostatistical calculations and graphical display of results. The text covers both essential and advanced techniques and applications in biostatistics that are relevant to epidemiology. This text is supplemented with teaching resources, including an online guide for students in solving exercises and an instructor's manual.

KEY FEATURES:

• First overview biostatistics textbook for epidemiology and public health that uses the open-source R program
• Covers essential and advanced techniques and applications in biostatistics as relevant to epidemiology
• Features abundant examples and exercises to illustrate the application of R language for biostatistical calculations and graphical displays of results
• Includes online student solutions guide and instructor's manual

Product Details

ISBN-13:	9780826110251
Publisher:	Springer Publishing Company
Publication date:	11/05/2015
Pages:	458
Sales rank:	873,087
Product dimensions:	7.00(w) x 9.90(h) x 1.00(d)

About the Author

Bertram K. C. (Bert) Chan, PhD, is currently Consulting Biostatistician at the School of Medicine, Department of Preventive Medicine at Loma Linda University.

Table of Contents

Contents

Preface

1. INTRODUCTION

1.1 Medicine, Preventive Medicine, Public Health, and Epidemiology

Medicine

Preventive Medicine and Public Health

Public Health and Epidemiology

Review Questions for Section 1.1

1.2 Personal Health and Public Health

Personal Health Versus Public Health

Review Questions for Section 1.2

1.3 Research and Measurements in EPDM and PH

EPDM: The Basic Science of PH

Main Epidemiologic Functions

The Cause of Diseases

Exposure Measurement in Epidemiology

Additional Issues

Review Questions for Section 1.3

1.4 BIOS and EPDM

Review Questions for Section 1.4

References

2. RESEARCH AND DESIGN IN EPIDEMIOLOGY AND PUBLIC HEALTH

Introduction

2.1 Causation and Association in Epidemiology and Public Health

The Bradford-Hill Criteria for Causation and Association in Epidemiology

Legal Interpretation Using Epidemiology

Disease Occurrence

Review Questions for Section 2.1

2.2 Causation and Inference in Epidemiology and Public Health

Rothman’s Diagrams for Sufficient Causation of Diseases

Causal Inferences

Using the Causal Criteria

Judging Scientific Evidence

Review Questions for Section 2.2

2.3 Biostatistical Basis of Inference

Modes of Inference

Levels of Measurement

Frequentist BIOS in EPDM

Confidence Intervals in Epidemiology and Public Health

Bayesian Credible Interval

Review Questions for Section 2.3

2.4 BIOS in EPDM and PH

Applications of BIOS

BIOS in EPDM and PH

Processing and Analyzing Basic Epidemiologic Data

Analyzing Epidemiologic Data

Using R

Evaluating a Single Measure of Occurrence

Poisson Count (Incidence) and Rate Data

Binomial Risk and Prevalence Data

Evaluating Two Measures of Occurrence—Comparison of Risk: Risk Ratio and Attributable Risk

Comparing Two Rate Estimates: Rate Ratio rr

Comparing Two Risk Estimates: Risk Ratio RR and Disease (Morbidity) Odds Ratio DOR

Comparing Two Odds Estimates From Case–Control: The Salk Polio Vaccine Epidemiologic Study

Review Questions for Section 2.4

Exercises for Chapter 2

Using Probability Theory

Disease Symptoms in Clinical Drug Trials

Risks and Odds in Epidemiology

Case–Control Epidemiologic Study

Mortality, Morbidity, and Fertility Rates

Incidence Rates in Case-Cohort Survival Analysis

Prevalence

Mortality Rates

Estimating Sample Sizes

References

Appendix

3. DATA ANALYSIS USING R PROGRAMMING

Introduction

3.1 Data and Data Processing

Data Coding

Data Capture

Data Editing

Imputations

Data Quality

Producing Results

Review Questions for Section 3.1

3.2 Beginning R

R and Biostatistics

A First Session Using R

The R Environment

Review Questions for Section 3.2

3.3 R as a Calculator

Mathematical Operations Using R

Assignment of Values in R and Computations Using Vectors and Matrices

Computations in Vectors and Simple Graphics

Use of Factors in R Programming

Simple Graphics

x as Vectors and Matrices in Biostatistics

Some Special Functions That Create Vectors

Arrays and Matrices

Use of the Dimension Function dim in R

Use of the Matrix Function matrix in R

Some Useful Functions Operating on Matrices in R

NA: “Not Available” for Missing Values in Datasets

Special Functions That Create Vectors

Review Questions for Section 3.3

Exercises for Section 3.3

3.4 Using R in Data Analysis in BIOS

Entering Data at the R Command Prompt

The Function list() and the Making of data.frame() in R

Review Questions for Section 3.4

Exercises for Section 3.4

3.5 Univariate, Bivariate, and Multivariate Data Analysis

Univariate Data Analysis

Bivariate and Multivariate Data Analysis

Multivariate Data Analysis

Analysis of Variance (ANOVA)

Review Questions for Section 3.5

Exercises for Section 3.5

References

Appendix: Documentation for the plot function

Generic X–Y Plotting

4. GRAPHICS USING R

Introduction

Choice of System

Packages

4.1 Base (or Traditional) Graphics

High-Level Functions

Low-Level Plotting Functions

Interacting with Graphics

Using Graphics Parameters

Parameters List for Graphics

Device Drivers

Review Questions for Section 4.1

Exercises for Section 4.1

4.2 Grid Graphics

The lattice Package: Trellis Graphics

The Grid Model for R Graphics

Grid Graphics Objects

Applications to Biostatistical and Epidemiologic Investigations

Review Questions for Section 4.2

Exercises for Section 4.2

References

5. PROBABILITY AND STATISTICS IN BIOSTATISTICS

Introduction

5.1 Theories of Probability

What Is Probability?

Basic Properties of Probability

Probability Computations Using R

Applications of Probability Theory to Health Sciences

Typical Summary Statistics in Biostatistics: Confidence Intervals, Significance Tests, and Goodness of Fit

Review Questions for Section 5.1

Exercises for Section 5.1

5.2 Typical Statistical Inference in Biostatistics: Bayesian Biostatistics

What Is Bayesian Biostatistics?

Bayes’s Theorem in Probability Theory

Bayesian Methodology and Survival Analysis (Time-to-Event) Models for Biostatistics in Epidemiology and Preventive Medicine

The Inverse Bayes Formula

Modeling in Biostatistics

Review Questions for Section 5.2

Exercises for Section 5.2

References

6. CASE–CONTROL STUDIES AND COHORT STUDIES IN EPIDEMIOLOGY

Introduction

6.1 Theory and Analysis of Case–Control Studies

Advantages and Limitations of Case–Control Studies

Analysis of Case–Control Studies

Review Questions for Section 6.1

Exercises for Section 6.1

6.2 Theory and Analysis of Cohort Studies

An Important Application of Cohort Studies

Clinical Trials

Randomized Controlled Trials

Cohort Studies for Diseases of Choice and Noncommunicable Diseases

Cohort Studies and the Lexis Diagram in the Biostatistics of Demography

Review Questions for Section 6.2

Exercises for Section 6.2

References

7. RANDOMIZED TRIALS, PHASE DEVELOPMENT, CONFOUNDING IN SURVIVAL ANALYSIS, AND LOGISTIC REGRESSIONS

7.1 Randomized Trials

Classifications of RTs by Study Design

Randomization

Biostatistical Analysis of Data from RTs

Biostatistics for RTs in the R Environment

Review Questions for Section 7.1

Exercises for Section 7.1

7.2 Phase Development

Phase 0 or Preclinical Phase

Phase I

Phase II

Phase III

Pharmacoepidemiology: A Branch of Epidemiology

Some Basic Tests in Epidemiologic Phase Development

Review Questions for Section 7.2

Exercises for Section 7.2

7.3 Confounding in Survival Analysis

Biostatistical Approaches for Controlling Confounding

Using Regression Modeling for Controlling Confounding

Confounding and Collinearity

Review Questions for Section 7.3

Exercises for Section 7.3

7.4 Logistic Regressions

Inappropriateness of the Simple Linear Regression When y Is a Categorical Dependent Variable

The Logistic Regression Model

The Logit

Logistic Regression Analysis

Generalized Linear Models in R

Review Questions for Section 7.4

Exercises for Section 7.4

References

Index

Editorial Reviews

Reviewer: Bhavesh Barad, MD (East Tennessee State University Quillen College of Medicine)
Description: The book describes the use of biostatistical analysis in epidemiology and public health science using R language.
Purpose: The book showcases topics to help readers become familiar with R language and its various applications in biostatistical analysis.
Audience: The intended audience is graduate students, professors, and professionals in the field of epidemiology and public health.
Features: This book introduces concepts, research, and measurements in epidemiology and public health, and also illustrates designs and research in the respective fields. It provides a comprehensive explanation for data analysis and graphics using R language, including how R language handles classic problems in case-control, cohort studies and its use in survival analysis. The book is written in a way to familiarize readers with R language and its applications dealing with epidemiological and preventive health science studies. Each chapter is presented in a systematic manner, with step-by-step narration. There is a brief introduction in each chapter, as well as graphs, formulas, algorithms, and tables. There are review questions at the end of each chapter, followed by references.
Assessment: The content and quality of this book is excellent. It is a great tool for understanding the use of R language for biostatistical analysis.

Doody's Review Service