Author Profile

Robert H. Davis, PhD, is a distinguished biostatistician, researcher, and educator with over three decades of academic and clinical experience in the application of statistical methods to biological and medical sciences. Known for his ability to translate intricate mathematical theories into practical, intuitive insights, Dr. Davis has dedicated his career to bridging the gap between rigorous data science and frontline healthcare research. Dr. Davis earned his Ph.D. in Biostatistics from the Harvard T.H. Chan School of Public Health, where his doctoral research focused on survival analysis models for chronic disease progression. Prior to his doctorate, he completed his Master of Science in Statistics at the University of Washington and graduated summa cum laude with a Bachelor’s degree in Mathematics. Currently, he serves as a Professor Emeritus of Biostatistics at a leading research university, where he previously chaired the Department of Epidemiology and Biostatistics for over ten years. Throughout his tenure, he has designed and taught foundational biostatistics courses for medical students, public health professionals, and undergraduate biology majors—an experience that directly inspired the structure and student-centric pedagogy of Introduction to Biostatistics. Beyond the classroom, Dr. Davis has been a foundational figure in clinical trial design and epidemiological research. He has served as the principal methodologist and lead statistician on numerous multi-center clinical trials funded by the National Institutes of Health (NIH) and the World Health Organization (WHO), spanning critical areas such as oncology, infectious diseases, and cardiovascular health.

About The Book

In the realms of medicine, public health, and the life sciences, data is constantly being generated. From the microscopic fluctuations of genetic sequences to the sprawling, macro-level dynamics of global pandemics, the living world is a beautiful, chaotic tapestry of variation. How do we make sense of this noise? How do we separate meaningful medical breakthroughs from mere coincidence?The answer lies in Biostatistics—the definitive science of dealing with uncertainties using incomplete information. Introduction to Biostatistics serves as an essential roadmap for students, researchers, and healthcare professionals navigating the quantitative demands of modern biological research. Tailored specifically for those who operate at the intersection of science and medicine, this comprehensive text strips away the traditional anxiety of dry mathematical formulas and replaces it with a vibrant, problem-based approach to learning. Whether you are a biologist looking to validate experimental data, a medical student deciphering clinical trials, or a public health professional tracking disease vectors, this book transforms statistics from a daunting academic hurdle into a critical "way of thinking." Rather than forcing readers to mindlessly memorize proof after proof, this text prioritizes conceptual clarity. It minimizes heavy algebraic jargon, focusing instead on why a statistical test works, when to deploy it, and how to interpret the results. Packed with authentic datasets from contemporary epidemiology, pharmacology, biochemistry, and environmental science, every chapter anchors abstract theory into concrete reality. Readers will actively engage with real problems faced by field researchers.

Table of Contents

Preface
1.DESCRIPTIVE METHODS FOR CATEGORICAL DATA

1.1 Proportions
1.2 Rates
1.3 Ratios
1.4 Notes on Computations
2. DESCRIPTIVE METHODS FOR CONTINUOUS DATA

2.1 Tabular and Graphical Methods
2.2 Numerical Methods
2.3 Special Case of Binary Data
2.4 Coefficients of Correlation
2.5 Notes on Computations
3. PROBABILITY AND PROBABILITY MODELS

3.1 Probability
3.2 Normal Distribution
3.3 Probability Models For Continuous Data
3.4 Probability Models For Discrete Data
3.5 Notes on Computations
4.ESTIMATION OF PARAMETERS

4.1 Basic Concepts
4.2 Estimation of Means
4.3 Estimation of Proportions
4.4 Estimation of Odds Ratios
4.5 Estimation of Correlation Coefficients
4.6 Brief Notes on The Fundamentals
4.7 Notes on Computations

5.COSMIDS, PHASMIDS, AND OTHER ADVANCED VECTORS

5.1 Basic Concepts
5.2 Analogies
5.3 Summaries and Conclusions
5.4 Brief Notes on The Fundamentals
6.GENE-CLONING STRATEGIES

6.1 One-Sample Problem With Binary Data
6.2 Analysis of Pair-Matched Data
6.3 Comparison of Two Proportions
6.4 Mantel–Haenszel Method
6.5 Inferences For General Two-Way Tables
6.6 Fisher’s Exact Test
6.7 Ordered 2d K Contingency Tables
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
7.5 One-Way Analysis of Variance
7.6 Brief Notes on The Fundamentals
7.7 Notes on Computations
8.CORRELATION AND REGRESSION

8.1 Simple Regression Analysis
8.2 Multiple Regression Analysis
8.3 Notes on Computations
9.LOGISTIC REGRESSION

9.1 Simple Regression Analysis
9.2 Multiple Regression Analysis
9.3 Brief Notes on The Fundamentals
10.METHODS FOR COUNT DATA

10.1 Poisson Distribution
10.2 Testing Goodness of Fit
10.3 Poisson Regression Model
11.STUDY DESIGNS

11.1 Types of Study Designs
11.2 Classification of Clinical Trials
11.3 Designing Phase I Cancer Trials
11.4 Sample Size Determination For Phase II Trials and Surveys
11.5 Sample Sizes For Other Phase II Trials
11.6 About Simon’s Two-Stage Phase II Design
11.7 Phase Ii Designs For Selection
11.8 Toxicity Monitoring In Phase Ii Trials
11.9 Sample Size Determination For Phase Iii Trials
BIBLIOGRAPHY
INDEX