Biostatistics: A Computing Approach

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.

Contents

Preface

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
Exercises

Use of Simulation Techniques
Introduction
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
Exercises

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

Correlation and Regression
Introduction
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
Exercises

Analysis of Variance
Introduction
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
Exercises

DiscreteMeasures of Risk
Introduction
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
Exercises

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

Analysis of Repeated Measures Data
Introduction
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
Exercises

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

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
Exercises

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
Exercises

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

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

References

Index

Customer Reviews