Biostatistics: A Computing Approach

By Stewart Anderson

Series Editors: Chris Chatfield, Bradley. P. Carlin, Martin A. Tanner, James V. Zidek

© 2012 – Chapman and Hall/CRC

326 pages | 65 B/W Illus.

Purchasing Options:
Hardback: 9781584888345
pub: 2011-12-20
US Dollars$92.95

Comp Exam Copy

About the Book

The emergence of high-speed computing has facilitated the development of many exciting statistical and mathematical methods in the last 25 years, broadening the landscape of available tools in statistical investigations of complex data. Biostatistics: A Computing Approach focuses on visualization and computational approaches associated with both modern and classical techniques. Furthermore, it promotes computing as a tool for performing both analyses and simulations that can facilitate such understanding.

As a practical matter, programs in R and SAS are presented throughout the text. In addition to these programs, appendices describing the basic use of SAS and R are provided. Teaching by example, this book emphasizes the importance of simulation and numerical exploration in a modern-day statistical investigation. A few statistical methods that can be implemented with simple calculations are also worked into the text to build insight about how the methods really work.

Suitable for students who have an interest in the application of statistical methods but do not necessarily intend to become statisticians, this book has been developed from Introduction to Biostatistics II, which the author taught for more than a decade at the University of Pittsburgh.


"The book presents important topics in biostatistics alongside examples provided in the programming languages SAS and R. … The book covers many relevant topics every student should know in a way that it makes it easy to follow … each chapter provides exercises encouraging the reader to deepen her/his understanding. I really like that the theory is presented in a clear manner without interruptions of example programs. Instead, the programs are always presented at the end of a section. … this book can serve as a good start for the more statistics inclined students who haven’t yet recognized that in order to become a good biostatistician, you need to be able to write your own code. … I can recommend to all serious students who want to get a thorough start into this field."

—Frank Emmert-Streib, Queen’s University Belfast, CHANCE, August 2013

Table of Contents


Review of Topics in Probability and Statistics

Introduction to Probability

Conditional Probability

Random Variables

The Uniform distribution

The Normal distribution

The Binomial Distribution

The Poisson Distribution

The Chi–Squared Distribution

Student’s t–distribution

The F-distribution

The Hypergeometric Distribution

The Exponential Distribution


Use of Simulation Techniques


What can we accomplish with simulations?

How to employ a simple simulation strategy

Generation of Pseudorandom Numbers

Generating Discrete and Continuous random variables

Testing Random Number Generators

A Brief Note on the Efficiency of Simulation Algorithms


The Central Limit Theorem


The Strong Law of Large Numbers

The Central Limit Theorem

Summary of the Inferential Properties of the Sample Mean

Appendix: Program Listings


Correlation and Regression


Pearson’s Correlation Coefficient

Simple Linear Regression

Multiple Regression

Visualization of Data

Model Assessment and Related Topics

Polynomial Regression

Smoothing Techniques

Appendix: A Short Tutorial in Matrix Algebra


Analysis of Variance


One–Way Analysis of Variance

General Contrast

Multiple Comparisons Procedures

Gabriel’s method

Dunnett’s Procedure

Two-Way Analysis of Variance: Factorial Design

Two-Way Analysis of Variance: Randomized Complete Blocks

Analysis of Covariance


DiscreteMeasures of Risk


Odds Ratio (OR) and Relative Risk (RR)

Calculating risk in the presence of confounding

Logistic Regression

Using SAS and R for Logistic Regression

Comparison of Proportions for Paired Data


Multivariate Analysis

The Multivariate Normal Distribution

One and Two Sample Multivariate Inference

Multivariate Analysis of Variance

Multivariate Regression Analysis

Classification Methods


Analysis of Repeated Measures Data


Plotting Repeated Measures Data

Univariate Approaches for the Analysis of Repeated Measures Data

Covariance Pattern Models

Multivariate Approaches

Modern Approaches for the Analysis of Repeated Measures Data

Analysis of Incomplete Repeated Measures Data




Comparing Paired Distributions

Comparing Two Independent Distributions

Kruskal–Wallis Test

Spearman’s rho

The Bootstrap


Analysis of Time to Event Data

Incidence Density (ID)

Introduction to Survival Analysis

Estimation of the Survival Curve

Estimating the Hazard Function

Comparing Survival in Two Groups

Cox Proportional Hazards Model

Cumulative Incidence


Sample size and power calculations

Sample sizes and power for tests of normally distributed data

Sample size and power for Repeated Measures Data

Sample size and power for survival analysis

Constructing Power Curves


Appendix A: Using SAS


Data input in SAS

Some Graphical Procdures: PROC PLOT and PROC CHART

Some Simple Data Analysis Procedures

Diagnosing errors in SAS programs


Appendix B: Using R


Getting started


Some Simple Data Analysis Procedures

Using R for plots

Comparing an R–session to a SAS session

Diagnosing problems in R programs




About the Series

Chapman & Hall/CRC Biostatistics Series

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General