Applied Medical Statistics Using SAS: 1st Edition (Hardback) book cover

Applied Medical Statistics Using SAS

1st Edition

By Geoff Der, Brian S. Everitt

Chapman and Hall/CRC

559 pages | 113 B/W Illus.

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pub: 2012-10-01
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Written with medical statisticians and medical researchers in mind, this intermediate-level reference explores the use of SAS for analyzing medical data. Applied Medical Statistics Using SAS covers the whole range of modern statistical methods used in the analysis of medical data, including regression, analysis of variance and covariance, longitudinal and survival data analysis, missing data, generalized additive models (GAMs), and Bayesian methods. The book focuses on performing these analyses using SAS, the software package of choice for those analysing medical data.


  • Covers the planning stage of medical studies in detail; several chapters contain details of sample size estimation
  • Illustrates methods of randomisation that might be employed for clinical trials
  • Covers topics that have become of great importance in the 21st century, including Bayesian methods and multiple imputation

Its breadth and depth, coupled with the inclusion of all the SAS code, make this book ideal for practitioners as well as for a graduate class in biostatistics or public health.

Complete data sets, all the SAS code, and complete outputs can be found on an associated website:


"Each chapter in the book is well laid out, contains examples with SAS code, and ends with a concise summary. The chapters in the book contain the right level of information to use SAS to apply different statistical methods. … a good overview of how to apply in SAS 9.3 the many possible statistical analysis methods."

—Caroline Kennedy, Takeda Development Centre Europe Ltd., Statistical Methods for Medical Research, 2015

"… a well-organized and thorough exploration of broad coverage in medical statistics. The book is an excellent reference of statistical methods with examples of medical data and SAS codes for statisticians or statistical analysts who are working in the medical/clinical area. It also can be a reference book for an introductory or intermediate graduate biostatistics course."

—Jun Zhao, Journal of Biopharmaceutical Statistics, 24, 2014

"A recent request to a statistical professional body by a doctor seeking help with analysing data they had collected was greeted with derision by some of the members of that body. … The doctor in question may have been better served by simply purchasing this wide-ranging and accessible book. Medical students would also appreciate the range of topics addressed. … I think consultant statisticians would also appreciate the refreshers/introductions to statistical techniques and the SAS code for each. Indeed SAS code is liberally scattered throughout the text, and a couple of SAS macros are referred to in the meta-analysis chapter. … The text is supported by ten pages of references and a sizeable index. The code and example data sets can be downloaded from the SAS website."

—Alice Richardson, International Statistical Review (2013), 81

"Applied Medical Statistics Using SAS is a thorough documentation of statistical methods, inclusive of medical data sets and SAS code. The book would make an excellent reference guide for medical data analysts with access to base SAS 9.3 or a textbook for an introductory and intermediate graduate biostatistics course. … [It] comes to the market at an appropriate time in the extension of statistical applications to the medical industry … The thoroughness of procedures and the consideration the authors included in the selection of graphs, SAS code, and theory allow this book to be a resourceful companion for medical analysts. If looking for a broad selection of medical analyses using base SAS 9.3, this is the book for you; in addition, if a particular topic is required for further analyses, the book references additional sources."

Journal of Statistical Software, Volume 52, January 2013

Table of Contents

An Introduction to SAS


The User Interface

SAS Programs

Reading Data—The Data Step

Modifying SAS Data

The Proc Step

Global Statements

SAS Graphics

ODS—The Output Delivery System

Saving Output in SAS Data Sets—ods output

Enhancing Output

SAS Macros

Some Tips for Preventing and Correcting Errors

Statistics and Measurement in Medicine


A Brief History of Medical Statistics

Measurement in Medicine

Assessing Bias and Reliability of Measurements

Diagnostic Tests


Clinical Trials


Clinical Trials

How Many Participants Do I Need in My Trial?

The Analysis of Data from Clinical Trials




Types of Epidemiological Study

Relative Risk and Odds Ratios

Sample Size Estimation for Epidemiologic Studies

Simple Analyses for Data from Observational Studies




Study Selection

Publication Bias

The Statistics of Meta-analysis

An Example of the Application of Meta-analysis

Meta-analysis on Sparse Data



Analysis of Variance and Covariance


A Simple Example of One-Way Analysis of Variance

Multiple Comparison Procedures

A Factorial Experiment

Unbalanced Designs

Nonparametric Analysis of Variance

Analysis of Covariance


Scatter Plots, Correlation, Simple Regression, and Smoothing


The Scatter Plot and Correlation Coefficient

Simple Linear Regression and Locally Weighted Regression

Locally Weighted Regression

The Aspect Ratio of a Scatter Plot

Estimating Bivariate Densities

Scatter Plot Matrices


Multiple Linear Regression


The Multiple Linear Regression Model

Some Examples of the Application of the Multiple Linear Regression Model

Identifying a Parsimonious Model

Checking Model Assumptions: Residuals and Other

Regression Diagnostics

The General Linear Model


Logistic Regression


Logistic Regression

Two Examples of the Application of Logistic Regression

Diagnosing a Logistic Regression Model

Logistic Regression for 1:1 Matched Studies

Propensity Scores


The Generalised Linear Model


Generalised Linear Models

Applying the Generalised Linear Model

Residuals for GLMs



Generalised Additive Models


Scatter Plot Smoothers

Additive and Generalised Additive Models

Examples of the Application of GAMs


The Analysis of Longitudinal Data I


Graphical Displays of Longitudinal Data

Summary Measure Analysis of Longitudinal Data

Summary Measure Approach for Binary Responses


The Analysis of Longitudinal Data II: Linear Mixed-Effects Models for Normal Response Variables


Linear Mixed-Effects Models for Repeated Measures Data

Dropouts in Longitudinal Data


The Analysis of Longitudinal Data III: Non-Normal Responses


Marginal Models and Conditional Models

Analysis of the Respiratory Data

Analysis of Epilepsy Data


Survival Analysis


The Survivor Function and the Hazard Function

Comparing Groups of Survival Times

Sample Size Estimation


Cox’s Proportional Hazards Models for Survival Data


Modelling the Hazard Function: Cox’s Regression

Time-Varying Covariates

Random-Effects Models for Survival Data


Bayesian Methods


Bayesian Estimation

Markov Chain Monte Carlo

Prior Distributions

Model Selection When Using a Bayesian Approach

Some Examples of the Application of Bayesian Statistics


Missing Values


Patterns of Missing Data

Missing Data Mechanisms

Exploring Missingness

Dealing with Missing Values

Imputing Missing Values

Analysing Multiply Imputed Data

Some Examples of the Application of Multiple Imputation



Subject Categories

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