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Full Description
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 analyzing 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.
Contents
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.
