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Applied Medical Statistics Using SAS

Geoff Der and Brian S. Everitt
Publisher: 
Chapman & Hall/CRC
Publication Date: 
2013
Number of Pages: 
541
Format: 
Hardcover
Price: 
89.95
ISBN: 
9781439867976
Category: 
Textbook
We do not plan to review this book.

An Introduction to SAS
Introduction
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

Introduction
A Brief History of Medical Statistics
Measurement in Medicine
Assessing Bias and Reliability of Measurements
Diagnostic Tests
Summary

Clinical Trials

Introduction
Clinical Trials
How Many Participants Do I Need in My Trial?
The Analysis of Data from Clinical Trials
Summary

Epidemiology

Introduction
Types of Epidemiological Study
Relative Risk and Odds Ratios
Sample Size Estimation for Epidemiologic Studies
Simple Analyses for Data from Observational Studies
Summary

Meta-analysis

Introduction
Study Selection
Publication Bias
The Statistics of Meta-analysis
An Example of the Application of Meta-analysis
Meta-analysis on Sparse Data
Metaregression
Summary

Analysis of Variance and Covariance

Introduction
A Simple Example of One-Way Analysis of Variance
Multiple Comparison Procedures
A Factorial Experiment
Unbalanced Designs
Nonparametric Analysis of Variance
Analysis of Covariance
Summary

Scatter Plots, Correlation, Simple Regression, and Smoothing

Introduction
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
Summary

Multiple Linear Regression

Introduction
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
Summary

Logistic Regression

Introduction
Logistic Regression
Two Examples of the Application of Logistic Regression
Diagnosing a Logistic Regression Model
Logistic Regression for 1:1 Matched Studies
Propensity Scores
Summary

The Generalised Linear Model

Introduction
Generalised Linear Models
Applying the Generalised Linear Model
Residuals for GLMs
Overdispersion
Summary

Generalised Additive Models

Introduction
Scatter Plot Smoothers
Additive and Generalised Additive Models
Examples of the Application of GAMs
Summary

The Analysis of Longitudinal Data I

Introduction
Graphical Displays of Longitudinal Data
Summary Measure Analysis of Longitudinal Data
Summary Measure Approach for Binary Responses
Summary

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

Introduction
Linear Mixed-Effects Models for Repeated Measures Data
Dropouts in Longitudinal Data
Summary

The Analysis of Longitudinal Data III: Non-Normal Responses

Introduction
Marginal Models and Conditional Models
Analysis of the Respiratory Data
Analysis of Epilepsy Data
Summary

Survival Analysis

Introduction
The Survivor Function and the Hazard Function
Comparing Groups of Survival Times
Sample Size Estimation
Summary

Cox’s Proportional Hazards Models for Survival Data

Introduction
Modelling the Hazard Function: Cox’s Regression
Time-Varying Covariates
Random-Effects Models for Survival Data
Summary

Bayesian Methods

Introduction
Bayesian Estimation
Markov Chain Monte Carlo
Prior Distributions
Model Selection When Using a Bayesian Approach
Some Examples of the Application of Bayesian Statistics
Summary

Missing Values

Introduction
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
Summary

References

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