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Biostatistics: A Computing Approach


Series: Chapman & Hall/CRC Biostatistics Series Volume: 48

By: Stewart Anderson(Author), Chris Chatfield(Editor), Bradley P Carlin(Editor), Martin A Tanner(Editor)

326 pages, 65 b/w illustrations, 7 tables

CRC Press

Hardback | Jan 2012 | #203894 | ISBN-13: 9781584888345
Availability: Usually dispatched within 6 days Details
NHBS Price: £41.99 $54/€47 approx

About this book

The emergence of high-speed computing has facilitated the development of many exciting statistical and mathematical methods in the last 25 years, broadening the landscape of available tools in statistical investigations of complex data. Biostatistics: A Computing Approach focuses on visualization and computational approaches associated with both modern and classical techniques. Furthermore, it promotes computing as a tool for performing both analyses and simulations that can facilitate such understanding. As a practical matter, programs in R and SAS are presented throughout the text. In addition to these programs, appendices describing the basic use of SAS and R are provided.

Teaching by example, Biostatistics: A Computing Approach emphasizes the importance of simulation and numerical exploration in a modern-day statistical investigation. A few statistical methods that can be implemented with simple calculations are also worked into the text to build insight about how the methods really work. Suitable for students who have an interest in the application of statistical methods but do not necessarily intend to become statisticians, Biostatistics: A Computing Approach has been developed from Introduction to Biostatistics II, which the author taught for more than a decade at the University of Pittsburgh.



Review of Topics in Probability and Statistics

Introduction to Probability
Conditional Probability
Random Variables
The Uniform distribution
The Normal distribution
The Binomial Distribution
The Poisson Distribution
The Chi–Squared Distribution
Student’s t–distribution
The F-distribution
The Hypergeometric Distribution
The Exponential Distribution

Use of Simulation Techniques
What can we accomplish with simulations?
How to employ a simple simulation strategy
Generation of Pseudorandom Numbers
Generating Discrete and Continuous random variables
Testing Random Number Generators
A Brief Note on the Efficiency of Simulation Algorithms

The Central Limit Theorem
The Strong Law of Large Numbers
The Central Limit Theorem
Summary of the Inferential Properties of the Sample Mean
Appendix: Program Listings

Correlation and Regression
Pearson’s Correlation Coefficient
Simple Linear Regression
Multiple Regression
Visualization of Data
Model Assessment and Related Topics
Polynomial Regression
Smoothing Techniques
Appendix: A Short Tutorial in Matrix Algebra

Analysis of Variance
One–Way Analysis of Variance
General Contrast
Multiple Comparisons Procedures
Gabriel’s method
Dunnett’s Procedure
Two-Way Analysis of Variance: Factorial Design
Two-Way Analysis of Variance: Randomized Complete Blocks
Analysis of Covariance

DiscreteMeasures of Risk
Odds Ratio (OR) and Relative Risk (RR)
Calculating risk in the presence of confounding
Logistic Regression
Using SAS and R for Logistic Regression
Comparison of Proportions for Paired Data

Multivariate Analysis
The Multivariate Normal Distribution
One and Two Sample Multivariate Inference
Multivariate Analysis of Variance
Multivariate Regression Analysis
Classification Methods

Analysis of Repeated Measures Data
Plotting Repeated Measures Data
Univariate Approaches for the Analysis of Repeated Measures Data
Covariance Pattern Models
Multivariate Approaches
Modern Approaches for the Analysis of Repeated Measures Data
Analysis of Incomplete Repeated Measures Data

Comparing Paired Distributions
Comparing Two Independent Distributions
Kruskal–Wallis Test
Spearman’s rho
The Bootstrap

Analysis of Time to Event Data
Incidence Density (ID)
Introduction to Survival Analysis
Estimation of the Survival Curve
Estimating the Hazard Function
Comparing Survival in Two Groups
Cox Proportional Hazards Model
Cumulative Incidence

Sample size and power calculations
Sample sizes and power for tests of normally distributed data
Sample size and power for Repeated Measures Data
Sample size and power for survival analysis
Constructing Power Curves

Appendix A: Using SAS
Data input in SAS
Some Graphical Procdures: PROC PLOT and PROC CHART
Some Simple Data Analysis Procedures
Diagnosing errors in SAS programs

Appendix B: Using R
Getting started
Some Simple Data Analysis Procedures
Using R for plots
Comparing an R–session to a SAS session
Diagnosing problems in R programs



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