Introduction to Biostatistical Applications in Health Research With - Microsoft Office Excel and

The second edition of Introduction to Biostatistical Applications in Health Research delivers a thorough examination of the basic techniques and most commonly used statistical methods in health research. Retaining much of what was popular with the well-received first edition, the thoroughly revised second edition includes a new chapter on testing assumptions and how to evaluate whether those assumptions are satisfied and what to do if they are not.
 
The newest edition contains brand-new code examples for using the popular computer language R to perform the statistical analyses described in the chapters within. You'll learn how to use Excel to generate datasets for R, which can then be used to conduct statistical calculations on your data.
 
The book also includes a companion website with a new version of BAHR add-in programs for Excel. This new version contains new programs for nonparametric analyses, Student-Newman-Keuls tests, and stratified analyses. Readers will also benefit from coverage of topics like:
* Extensive discussions of basic and foundational concepts in statistical methods, including Bayes' Theorem, populations, and samples
* A treatment of univariable analysis, covering topics like continuous dependent variables and ordinal dependent variables
* An examination of bivariable analysis, including regression analysis and correlation analysis
* An analysis of multivariate calculations in statistics and how testing assumptions, like assuming Gaussian distributions or equal variances, affect statistical outcomes
 
Perfect for health researchers of all kinds, Introduction to Biostatistical Applications in Health Research also belongs on the bookshelves of anyone who wishes to better understand health research literature. Even those without a great deal of mathematical background will benefit greatly from this text.

List of contents

Preface
 
PART ONE: Basic Concepts
 
1.Thinking About Chance
 
1.1.Properties of Chance
 
1.2.Combinations of events
 
1.2.1 Intersections
 
1.2.2 Unions
 
1.3.Bayes' theorem
 
2.Describing Populations
 
2.1.Types of data
 
2.2.Describing distributions graphically
 
2.2.1. Graphing discrete data
 
2.2.2. Graphing continuous data
 
2.2.3. Frequency polygon
 
2.3.Describing distributions mathematically
 
2.3.1. Parameter of location
 
2.3.2. Parameter of dispersion
 
2.4 Taking chance into account
 
2.4.1 Standard normal distribution
 
3.Examining Samples
 
3.1.Nature of samples
 
3.2.Estimation
 
3.2.1 Point estimates
 
3.2.2 The sampling distribution
 
3.2.3 Interval estimates
 
3.3.Hypothesis testing
 
PART TWO: Univariable Analysis
 
4.Univariable Analysis of a Continuous Dependent Variable
 
4.1.Student's t distribution
 
4.2.Interval estimation
 
4.3.Hypothesis testing
 
5.Univariable Analysis of an Ordinal Dependent Variable
 
5.1 Nonparametric methods
 
5.2 Estimation
 
5.3 Wilcoxon signed-rank test
 
5.4 Statistical power of nonparametric tests
 
6.Univariable Analysis of a Nominal Dependent Variable
 
6.1.Distributions of nominal data
 
6.2.Point estimates
 
6.2.1 Proportions
 
6.2.2 Rates
 
6.3.Sampling distributions
 
6.3.1 Binomial distribution
 
6.3.2 Poisson distribution
 
6.4.Interval estimation
 
6.5.Hypothesis testing
 
PART THREE: Bivariable Analysis
 
7.Bivariable Analysis of a Continuous Dependent Variable
 
7.1.Continuous independent variable
 
7.1.1 Regression analysis
 
7.1.2 Correlation analysis
 
7.2.Ordinal independent variable
 
7.3.Nominal independent variable
 
7.3.1 Estimating the difference between groups
 
7.3.2 Taking chance into account
 
8.Bivariable Analysis of an Ordinal Dependent Variable
 
8.1.Ordinal independent variable
 
8.2.Nominal independent variable
 
9.Bivariable Analysis of a Nominal Dependent Variable
 
9.1.Continuous independent variable
 
9.1.1 Estimation
 
9.1.2 Hypothesis testing
 
9.2.Nominal independent variable
 
9.2.1 Dependent variable not affected by time: Unpaired design
 
9.2.2 Dependent variable not affected by time: Paired design
 
9.2.3 Dependent variable affected by time
 
PART FOUR: Multivariable Analysis
 
10.Multivariable Analysis of a Continuous Dependent Variable
 
10.1.Continuous independent variables
 
10.1.1 Multiple regression analysis
 
10.1.2 Multiple correlation analysis
 
10.2.Nominal independent variables
 
10.2.1 Analysis of variance
 
10.2.2 Posterior tests
 
10.3.Continuous and nominal independent variables
 
10.3.1 Indicator ("dummy") variables
 
10.3.2 Interaction variables
 
10.3.3 General linear model
 
11.Multivariable Analysis of an Ordinal Dependent Variable
 
11.1.Nonparametric ANOVA
 
11.2.Posterior testing
 
12.Multivariable Analysis of a Nominal Dependent Variable
 
12.1.Continuous and/or nominal independent variables
 
12.1.1 Maximum likelihood estimation
 
12.1.2 Logistic regression analysis
 
12.1.3 Cox regression analysis
 
12.2.Nominal independent variables
 
12.2.1 Stratified analysis
&nbs

About the author

ROBERT P. HIRSCH, PHD, is on the faculty at the Foundation for Advanced Education in the Sciences as well as a Medical Research Consultant with over thirty years of experience. He received his doctorate in Biology at Kansas State University. He was formerly Professor at the George Washington University - Columbian College of Arts & Science where he helped to develop the Epidemiology and Biostatistics Programs.

Summary

The second edition of Introduction to Biostatistical Applications in Health Research delivers a thorough examination of the basic techniques and most commonly used statistical methods in health research. Retaining much of what was popular with the well-received first edition, the thoroughly revised second edition includes a new chapter on testing assumptions and how to evaluate whether those assumptions are satisfied and what to do if they are not.

The newest edition contains brand-new code examples for using the popular computer language R to perform the statistical analyses described in the chapters within. You'll learn how to use Excel to generate datasets for R, which can then be used to conduct statistical calculations on your data.

The book also includes a companion website with a new version of BAHR add-in programs for Excel. This new version contains new programs for nonparametric analyses, Student-Newman-Keuls tests, and stratified analyses. Readers will also benefit from coverage of topics like:
* Extensive discussions of basic and foundational concepts in statistical methods, including Bayes' Theorem, populations, and samples
* A treatment of univariable analysis, covering topics like continuous dependent variables and ordinal dependent variables
* An examination of bivariable analysis, including regression analysis and correlation analysis
* An analysis of multivariate calculations in statistics and how testing assumptions, like assuming Gaussian distributions or equal variances, affect statistical outcomes

Perfect for health researchers of all kinds, Introduction to Biostatistical Applications in Health Research also belongs on the bookshelves of anyone who wishes to better understand health research literature. Even those without a great deal of mathematical background will benefit greatly from this text.

Report

"This book provides a good introduction to biostatistics with a lot of medical examples and exercises. It is perfect for those that have basic notions on mathematics, explaining the main formulas necessary for describing, testing and finding out the relationships between data ... the manuscript is very good, comprehensive in information, the chapters are well structured, it includes a great arsenal of examples analysed and described in the field of biostatistics at a basic level. The reader should achieve a solid first step knowledge in the area, both for the statistical concepts and also practical applications." - International Society for Clinical Biostatistics News

Product details

• Authors: Robert P Hirsch, Robert P. Hirsch, Rp Hirsch, Hirsch Robert P.
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 31.12.2020
• EAN: 9781119722595
• ISBN: 978-1-119-72259-5
• No. of pages: 640
• Subjects: Natural sciences, medicine, IT, technology > Mathematics; Statistik, Datenanalyse, Excel, Statistics, Biostatistik, Microsoft Excel, data analysis, Biostatistics, End-User Computing, Computer-Ratgeber