Main description:

Praise for the First Edition

Students from health, medical, pharmacy, and nursing will find...Introductory Biostatistics extremely useful. Difficult biostatistical concepts are made easier by simple and careful explanations..."

– Journal of Statistical Computation and Simulation

Maintaining the same accessible and hands–on presentation, Introductory Biostatistics, Second Edition continues to provide an organized introduction to basic statistical concepts commonly applied in research across the health sciences. With plenty of real–world examples, the new edition provides a practical, modern approach to the statistical topics found in the biomedical and public health fields.

Beginning with an overview of descriptive statistics in the health sciences, the book delivers topical coverage of probability models, parameter estimation, and hypothesis testing. Subsequently, the book focuses on more advanced topics with coverage of regression analysis, logistic regression, methods for count data, analysis of survival data, and designs for clinical trials. Featuring a thorough update,
Introductory Biostatistics, Second Edition includes:

• A new chapter on the use of higher order Analysis of Variance (ANOVA) in factorial and block designs


• A new chapter on testing and inference methods for repeatedly measured outcomes including continuous, binary, and count outcomes


• R incorporated throughout along with SAS^®, allowing readers to replicate results from presented examples with either software


• Multiple additional exercises, with partial solutions available to aid comprehension of crucial concepts


• Notes on Computations sections to provide further guidance on the use of software


• A related website that hosts the large data sets presented throughout the book

Introductory Biostatistics, Second Edition is an excellent textbook for upper–undergraduate and graduate students in introductory biostatistics courses. The book is also an ideal reference for applied statisticians working in the fields of public health, nursing, dentistry, and medicine.

Chap T. Le, PhD, is Distinguished Professor of Biostatistics and Director of Biostatistics and Bioinformatics at the University of Minnesota Masonic Cancer Center. He has provided statistical consulting for a variety of biomedical research projects, and he has worked on collaborations that have focused on the analyses of survival and categorical data and, currently, in the areas of cancer and tobacco research. Dr. Le is the author of Health and Numbers: A Problems–Based Introduction to Biostatistics, Third Edition; Applied Categorical Data Analysis and Translational Research, Second Edition and Applied Survival Analysis, all published by Wiley.

Lynn E. Eberly, PhD, is Associate Professor in the Division of Biostatistics at the University of Minnesota. The author of more than 100 journal articles, Dr. Eberly has been a statistical collaborator in biomedical and public health research for more than 15 years. Her current research interests include methods for and applications to correlated data in neurodegenerative conditions, endocrinology, psychiatry/psychology, and cancer research. 

Back cover:

Praise for the First Edition

Students from health, medical, pharmacy, and nursing will find...Introductory Biostatistics extremely useful. Difficult biostatistical concepts are made easier by simple and careful explanations..."

– Journal of Statistical Computation and Simulation

Maintaining the same accessible and hands–on presentation, Introductory Biostatistics, Second Edition continues to provide an organized introduction to basic statistical concepts commonly applied in research across the health sciences. With plenty of real–world examples, the new edition provides a practical, modern approach to the statistical topics found in the biomedical and public health fields.

Beginning with an overview of descriptive statistics in the health sciences, the book delivers topical coverage of probability models, parameter estimation, and hypothesis testing. Subsequently, the book focuses on more advanced topics with coverage of regression analysis, logistic regression, methods for count data, analysis of survival data, and designs for clinical trials. Featuring a thorough update,
Introductory Biostatistics, Second Edition includes:

• A new chapter on the use of higher order Analysis of Variance (ANOVA) in factorial and block designs


• A new chapter on testing and inference methods for repeatedly measured outcomes including continuous, binary, and count outcomes


• R incorporated throughout along with SAS^®, allowing readers to replicate results from presented examples with either software


• Multiple additional exercises, with partial solutions available to aid comprehension of crucial concepts


• Notes on Computations sections to provide further guidance on the use of software


• A related website that hosts the large data sets presented throughout the book

Introductory Biostatistics, Second Edition is an excellent textbook for upper–undergraduate and graduate students in introductory biostatistics courses. The book is also an ideal reference for applied statisticians working in the fields of public health, nursing, dentistry, and medicine.

Chap T. Le, PhD, is Distinguished Professor of Biostatistics and Director of Biostatistics and Bioinformatics at the University of Minnesota Masonic Cancer Center. He has provided statistical consulting for a variety of biomedical research projects, and he has worked on collaborations that have focused on the analyses of survival and categorical data and, currently, in the areas of cancer and tobacco research. Dr. Le is the author of Health and Numbers: A Problems–Based Introduction to Biostatistics, Third Edition; Applied Categorical Data Analysis and Translational Research, Second Edition and Applied Survival Analysis, all published by Wiley.

Lynn E. Eberly, PhD, is Associate Professor in the Division of Biostatistics at the University of Minnesota. The author of more than 100 journal articles, Dr. Eberly has been a statistical collaborator in biomedical and public health research for more than 15 years. Her current research interests include methods for and applications to correlated data in neurodegenerative conditions, endocrinology, psychiatry/psychology, and cancer research. 

Contents:

Preface to the First Edition

Preface to the Second Edition

Chapters

1. Descriptive Methods for Categorical Data

1.1 Proportions

1.1.1 Comparative studies

1.1.2 Screening tests

1.1.3 Displaying proportions

1.2 Rates

1.2.1 Changes

1.2.2 Measures of morbidity and mortality

1.2.3 Standardization of rates

1.3 Ratios

1.3.1 Relative risk

1.3.2 Odds and odds ratio

1.3.3 Generalized odds for ordered 2xk tables

1.3.4 The Mantel–Haenszel method

1.3.5 Standardized mortality ratio

1.4 Notes on Computations

Exercises

2. Descriptive Methods Continuous Data

2.1 Tabular and Graphical Methods

2.1.1 One–way scatter plots

2.1.2 Frequency distribution

2.1.2 Histogram and the frequency polygon

2.1.4 Cumulative frequency graph and percentiles

2.1.5 Stem–and–leaf diagrams

2.2 Numerical Methods

2.2.1 Mean

2.2.2 Other measures of location

2.2.3 Measures of dispersion

2.2.4 Box plots

2.3 The Special Case of Binary Data

2.4 Coefficient of Correlation

2.3.1 Pearson s correlation coefficient

2.3.2 Nonparametric correlation coefficients

2.5 Notes on Computations

Exercises

3. Probability and Probability Models

3.1 Probability

3.1.1 The certainty of uncertainty

3.1.2 Probability

3.1.3 Statistical relationship

3.1.4 Using screening tests

3.1.5 Measuring agreement

3.2 The Normal Distribution

3.2.1 Shape of the normal curve

3.2.2 Areas under the standard normal curve

3.2.3 The Normal as a probability model

3.3 Probability Models for Continuous Data

3.4 Probability Models for Discrete Data

3.4.1 The Binomial distribution

3.4.2 The Poisson distribution

3.5 Brief Notes on the Fundamentals

3.5.1 Mean and Variance

3.5.2 The Pair–matched Case–Control Study

3.6 Notes on Computations

Exercises

4. Estimation of Parameters

4.1 Basic Concepts

4.1.1 Statistics as variables

4.1.2 Sampling distributions

4.1.3 Introduction to confidence estimation

4.2 Estimation of Means

4.2.1 Confidence intervals for a mean

4.2.2 Use of small samples

4.2.3 Evaluation of interventions

4.3 Estimation of Proportions

4.4 Estimation of Odds Ratios

4.5 Estimation of Correlation Coefficients

4.6 Brief Notes on the Fundamentals

4.6.1 Maximum Likelihood Estimation

4.6.2 The Matched Case–Control Studies

4.7 Notes on Computations

Exercises

5. Introduction to Statistical Tests of Significance

5.1 Basic Concepts

5.1.1 Hypothesis tests

5.1.2 Statistical evidence

5.1.3 Errors

5.2 Analogies

5.2.1 Trials by jury

5.2.2 Medical screening tests

5.2.3 Common expectation

5.3 Summaries and Conclusions

5.3.1 Rejection region

5.3.2 p–Values

5.3.3 Relationship to confidence intervals

5.4 Brief Notes on the Fundamentals

5.4.1 Type I and Type II Errors

5.4.2 More About Errors and p–Values

Exercises

6. Comparison of Population Proportions

6.1 One–sample Problem with Binary Data

6.2 Analysis of Pair–matched Data

6.3 Comparison of Two Proportions

6.4 The Mantel–Haenszel Method

6.5 Inferences for General Two–way Tables

6.6 Fisher s Exact Test

6.7 Ordered 2xk Contingency Tables

6.8 Notes on Computations

Exercises

7. Comparison of Population Means

7.1 One–sample Problem with Continuous Data

7.2 Analysis of Pair–matched Data

7.3 Comparison of Two Means

7.4 Nonparametric Methods

7.4.1 The Wilcoxon rank–sum test

7.4.2 The Wilcoxon signed–rank test

7.5 One–way Analysis of Variance (ANOVA)

7.5.1 One–way Analysis of Variance Model

7.5.2 Group Comparisons

7.6 Brief Notes on the Fundamentals

7.7 Notes on Computations

Exercises

8. Analysis of Variance

8.1 Factorial Studies

8.2.1 Two Crossed Factors

8.2.2 Extensions To More Than Two Factors

8.2 Block Designs

8.3.1 Purpose

8.3.2 Fixed Block Designs

8.3.3 Random Block Designs

8.3 Diagnostics

Exercises

9. Regression Analysis

9.1 Simple Regression Analysis

9.1.1 Correlation and regression

9.1.2 The simple linear regression model

9.1.3 The scatter diagram

9.1.4 Meaning of regression parameters

9.1.5 Estimation of parameters

9.1.6 Testing for independence

9.1.7 Analysis of Variance approach

9.1.8 Some biomedical applications

9.2 Multiple Regression Analysis

9.2.1 Regression model with several independent variables

9.2.2 Meaning of regression parameters

9.2.3 Effect modifications

9.2.4 Polynomial Regression

9.2.5 Estimation of parameters

9.2.6 Analysis of Variance approach

9.2.7 Testing hypotheses in multiple linear regression

9.2.8 Some biomedical applications

9.3 Graphical and Computational Aids

Exercises

10. Logistic Regression

10.1 Simple Regression Analysis

10.1.1 The simple logistic regression model

10.1.2 Measure of association

10.1.3 The effects of measurement scale

10.1.4 Tests of association

10.1.5 The use of logistic model for different designs

10.1.6 Overdispersion

10.2 Multiple Regression Analysis

10.2.1 Logistic regression model with several covariates

10.2.2 Effect modifications

10.2.3 Polynomial Regression

10.2.4 Testing hypotheses in multiple logistic regression

10.2.5 The receiver operating characteristic (ROC) curve

10.2.6 ROC curve and logistic regression

10.3 Brief Notes on the Fundamentals

10.3 Notes on Computations

Exercises

11. Methods for Count Data

11.1 The Poisson Distribution

11.2 Testing Goodness–of–Fit

11.3 The Poisson Regression Model

11.3.1 The simple regression analysis

11.3.2 The multiple regression analysis

11.3.3 Overdispersion

11.3.4 Stepwise regression

Exercises

12. Methods for Repeatedly Measured Outcomes

12.1 Overview

12.2 Continuous outcomes

12.2.1 Extending regression using the Linear Mixed Model

12.2.2 Testing and inference

12.2.3 Special cases: random block designs and crossover designs

12.3 Binary outcomes

12.3.1 Extending logistic regression using Generalized Estimating Equations

12.3.2 Testing and inference

12.4 Count outcomes

12.4.1 Extending Poisson regression using Generalized Estimating Equations

12.4.2 Testing and inference

Exercises

13. Analysis of Survival Data and Data from Matched Studies

13.1 Survival Data

13.2 Introductory Survival Analyses

13.2.1 Kaplan–Meier curve

13.2.2 Comparison of survival distributions

13.3 Simple Regression and Correlation

13.3.1 Model and approach

13.3.2 Measures of association

13.3.3 Tests of association

13.4 Multiple Regression and Correlation

13.4.1 Proportional hazards model with several covariates

13.4.2 Testing hypotheses in multiple regression

13.4.3 Time–dependent covariates and applications

13.5 Pair–matched Case–Control Studies

13.5.1 The model

13.5.2 The analysis

13.6 Multiple Matching

13.6.1 The conditional approach

13.6.2 Estimation of the odds ratio

13.6.3 Testing for exposure effect

13.7 Conditional Logistic Regression

13.7.1 Simple regression analysis

13.7.2 Multiple regression analysis

Exercises

14. Designs for Clinical Studies

14.1 Types of Study Designs

14.2 Classification of Clinical Trials

14.3 Designing Phase I Cancer Trials

14.4 Sample Size Determination for Phase II Trials and Surveys

14.5 Sample Size Determination for Other Phase II Trials

14.5.1 Continuous endpoints

14.5.2 Correlation endpoints

14.6 About Simon s Two–stage Phase II Design

14.7 Phase II Designs for Selection

14.7.1 Continuous endpoints

14.7.2 Binary endpoints

14.8 Toxicity Monitoring in Phase II Trials

14.9 Sample Size Determination for Phase III Trials

14.9.1 Comparison of two means

14.9.2 Comparison of two proportions

14.9.3 Survival time as the endpoint

14.10 Sample Size Determination for Case–Control Studies

14.10.1 Unmatched designs for a binary exposure

14.10.2 Matched designs for a binary exposure

14.10.3 Unmatched designs for a continuous exposure

Exercises

Bibliography

Appendices

Answers to Exercises

Index