Biostatistics: A Computing Approach
14%
off

Biostatistics: A Computing Approach

By (author)  , Series edited by  , Series edited by  , Series edited by  , Series edited by 

Free delivery worldwide

Available. Dispatched from the UK in 2 business days
When will my order arrive?

Description

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, this book 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, this book has been developed from Introduction to Biostatistics II, which the author taught for more than a decade at the University of Pittsburgh.show more

Product details

  • Hardback | 326 pages
  • 156 x 236 x 20mm | 589.67g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 65 black & white illustrations, 7 black & white tables
  • 1584888342
  • 9781584888345
  • 1,833,492

Review quote

"The book presents important topics in biostatistics alongside examples provided in the programming languages SAS and R. ... The book covers many relevant topics every student should know in a way that it makes it easy to follow ... each chapter provides exercises encouraging the reader to deepen her/his understanding. I really like that the theory is presented in a clear manner without interruptions of example programs. Instead, the programs are always presented at the end of a section. ... this book can serve as a good start for the more statistics inclined students who haven't yet recognized that in order to become a good biostatistician, you need to be able to write your own code. ... I can recommend to all serious students who want to get a thorough start into this field." -Frank Emmert-Streib, Queen's University Belfast, CHANCE, August 2013show more

About Stewart Anderson

University of Pittsburgh, Pittsburgh, PA University of Bath, UK University of Minnesota, Minneapolis, USA Northwestern University, Evanston, Illinois, USA University of British Columbia, Vancouver, Canadashow more

Table of 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 Indexshow more