1st Edition

Applied Medical Statistics Using SAS

By Geoff Der, Brian S. Everitt Copyright 2013
    560 Pages 113 B/W Illustrations
    by Chapman & Hall

    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

    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



    Geoff Der, Brian S. Everitt

    "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