Biostatistics in Diabetes Care for Diabetologists, Endocrinologists, and PG Medical Students | Clinical Trial Design, Regression, Survival Analysis, and Machine Learning for Diabetes Research: 1

Biostatistics in Diabetes Care: A Comprehensive Guide for Evidence-Based Practice, 1/E applies core statistical methods directly to diabetes research and clinical decision-making, rather than teaching statistics in the abstract. Structured across 19 chapters organized into four progressive sections - Foundations of Biostatistics, Inferential Statistics and Hypothesis Testing, Risk Assessment and Predictive Modeling, and Advanced Analytical Techniques - the book takes the reader from basic descriptive statistics and probability through to survival analysis, regression modeling, machine learning, and meta-analysis, all illustrated with diabetes-specific examples. Each chapter opens with a boxed Learning Objective , uses bulleted lists of clinically framed key questions and bolded/italicized key terms to define concepts as they are introduced, walks through a fully worked numbered example (such as evaluating a new HbA1c-lowering drug through a step-by-step randomized controlled trial analysis), and closes with a Further Readings list of indexed-journal citations - giving the book both a teaching structure and a ready reference for literature review and thesis writing. Who This Book Is For: Diabetologists: practicing diabetes specialists who need to interpret clinical trial data, evaluate risk, and apply evidence-based statistical reasoning in patient management. Endocrinologists: specialists using biostatistical methods to critically appraise diabetes and endocrine research literature. Postgraduate Medical Students: PG scholars preparing theses, dissertations, or research papers in diabetes and endocrinology who need a structured, application-based grounding in biostatistics. Healthcare Professionals and Researchers: clinical researchers and allied healthcare professionals engaged in diabetes-related clinical trials, surveillance studies, or outcomes research. Why This Book Helps: Translates statistics into clinical language: every statistical concept (mean, variance, p-values, confidence intervals) is explained using real diabetes parameters such as HbA1c and fasting blood glucose rather than abstract numbers. Builds a stepwise foundation: chapters progress logically from data types and descriptive statistics to advanced inferential and predictive methods, so readers without a statistics background can follow along. Demystifies clinical trial interpretation: dedicated chapters on hypothesis testing, confidence intervals, regression, and survival analysis (Kaplan-Meier curves, Cox Proportional Hazards) equip readers to critically read and design diabetes research studies. Covers modern research tools: chapters on machine learning (decision trees, neural networks) and meta-analysis (fixed and random effects models, forest plots) bring the book up to date with current diabetes research methodology. Supports academic and publication needs: chapter-end Further Reading lists of indexed-journal citations aid thesis writing, literature reviews, and manuscript preparation. Key Features: 19 chapters across 4 structured sections, progressing from foundational biostatistics to advanced analytics in diabetes research. Boxed Learning Objectives and a fully worked, step-by-step numbered example (RCT-based drug-effectiveness analysis) in every chapter. Dedicated coverage of survival analysis, regression modeling (simple linear, multiple, logistic), and risk assessment (Odds Ratios, Relative Risk, Number Needed to Treat). Modern analytical techniques included: Machine Learning applications and Meta-analysis methodology tailored to diabetes research. Chapter-end Further Readings drawn from indexed medical journals, supporting thesis and research-paper preparation.

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