Biostatistics for Epidemiology and Public Health Using R 1st edition by Bertram Chan 9780826110268 0826110266

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ISBN 10: 0826110266
ISBN 13: 9780826110268 
Author: Bertram Chan

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.

Biostatistics for Epidemiology and Public Health Using R 1st Table of contents:

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

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

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