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Applied Medical Statistics Using SAS - 
      Geoff Der
    
      Brian S. Everitt

Applied Medical Statistics Using SAS

; Brian S. Everitt

«<p>"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."<br />—Caroline Kennedy, Takeda Development Centre Europe Ltd., <em>Statistical Methods for Medical Research</em>, 2015</p> <p>"… 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." <br />—Jun Zhao, <em>Journal of Biopharmaceutical Statistics</em>, 24, 2014</p> <p>"A recent request to a statistical professional bod»

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. Les mer
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Vår pris: 2025,-

(Innbundet) Fri frakt!
Leveringstid: Sendes innen 21 dager

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.





Features








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
FAKTA
Utgitt:
Forlag: Taylor & Francis Inc
Innbinding: Innbundet
Språk: Engelsk
Sider: 559
ISBN: 9781439867976
Format: 23 x 16 cm
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VURDERING
Gi vurdering
Les vurderinger
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