Supported by real-world case studies, this essential textbook provides a detailed overview of the use of biostatistical tools and methods, enabling students and researchers to undertake their own research with confidence and understanding.
After a general introduction to the field, the book provides a step-by-step description of the essential statistical methods that are foundational to analysing data from clinical trials, epidemiological studies and other health-related research. From basic concepts such as probability and distribution through to hypothesis testing, regression analysis, survival analysis, meta-analysis and systematic reviews, each chapter is designed with a clear pedagogical approach featuring explanatory diagrams, real-life examples and sample problems. Later sections of the book cover clinical trial design and analysis, diagnostic testing, Bayesian methods and machine learning. Through this detailed, comprehensive treatment of the key tools and methods, the book encourages readers to develop their own critical thinking skills, recognising good or bad pieces of research when they see them, asking questions about where evidence and assumptions come from or choosing the most appropriate biostatistical methodologies in their own research.
Written by a team of experts with extensive teaching experience in this field, this is the ideal textbook for graduate students and researchers across the biomedical sciences, from public health to epidemiology to clinical medicine.
Supported by real-world case studies, this essential textbook provides a detailed overview of the use of biostatistical tools and methods, enabling students and researchers to undertake their own research with confidence and understanding.
Part I Basic Statistics, Probability, and Statistical Hypothetical
Testing
1. Introduction to Biostatistics.
2. Descriptive Statistics in Health
Research: Methods for Summarizing Health-Related Data.
3. Exploratory Data
Analysis in Health Research Describing Measures and Methods for Summarizing
Health-Related Data.
4. Introduction to Probability.
5. Statistical
Hypothesis Testing for Health Sciences: Parametric and Non-Parametric. Part
II Regression Models
6. Regression Analysis through illustrations.
7.
Statistical Model for Measuring Awareness About the Use of Pentavalent
Vaccination Among Infants Through NFHS Data.
8. Addressing Confounding in
Health Science Research. Part III Survival Analysis, Meta-Analysis and
Systematic Reviews
9. Survival Analysis of Factors Associated with
Progression Of Cervical Cancer Patients.
10. Systematic Review and
Meta-analysis: Unveiling Evidence in Health Research.
11. A Step-By-Step
Guide on Writing a Systematic Literature Review for Prevalence Study.
12.
Meta-Analysis: A Primer. Part IV Clinical Trials Design and Analysis
13.
Conceptualization of Clinical Study Design: Insights and Utilities.
14.
Sample Size: Basic Concepts. Part V Diagnostic Tests
15. Inter-Observer and
Intra-Observer Reliability.
16. A Biostatistical Evaluation of Diabetes
Medication based on HbA1c and Glucose Levels among U.S. Adults. Part VI
Bayesian Methods and Machine Learning
17. Applying Bayesian Methods in
Diagnostics Tests for Clinical Decision-making.
18. Prediction of 90-Day
Mortality Using Machine Learning Algorithm.
Vivek Verma, Assistant Professor, Department of Statistics, Assam University Silchar, Assam, India.
Hafiz T.A. Khan, Professor of Public Health and Statistics at the University of West London and a Professorial Fellow at the Oxford Institute of Population Ageing, University of Oxford, UK. For the last three decades, he has been involved in teaching applied statistics, public health, demography, health economics and research methods. He has published more than 260 research articles in various international journals.
Dilip C. Nath, Former Vice-Chancellor of Assam University and Professor of Statistics at Gauhati University, India. He has published more than 200 research papers in reputed national and international journals. His current research interests are in biostatistics, medical statistics, demography and actuarial statistics.
Kenneth C. Land, John Franklin Crowell Professor Emeritus of Sociology at Duke University, where he is also a research professor at the Social Science Research Institute. He worked as faculty in the University of Illinois at Urbana-Champaign and the University of Texas at Austin and has published more than 450 research papers in various international journals.
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