1st Edition

Applied Medical Statistics Using SAS

ISBN 9781439867976
Published October 1, 2012 by Chapman and Hall/CRC
559 Pages 113 B/W Illustrations

USD $150.00

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Book Description

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: http://support.sas.com/amsus

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


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"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