Modern Inference Based on Health-Related Markers 1st Edition

Modern Inference Based on Health-Related Markers, 1st Edition by Albert Vexler, Jihnhee Yu, and Jiaojiao Zhou offers a timely, practical guide to the statistical tools driving decisions in biomarker research and clinical practice. Ideal for biostatisticians, epidemiologists, clinicians, and graduate students, this book bridges theory and applied methods for drawing robust inferences from health-related markers.

Start with clear, real-world examples that make complex concepts accessible. The authors present contemporary inference techniques—model-based approaches, nonparametric methods, and predictive modeling—alongside assessments of diagnostic accuracy, longitudinal marker analysis, and causal interpretation. Emphasis on reproducible, transparent analysis prepares readers for high-impact work in clinical trials, observational studies, and public health surveillance.

Why this book matters: it equips professionals with the statistical reasoning and practical workflows needed to evaluate biomarkers across populations and settings—from hospital systems in the United States to research centers in Europe and Asia. Readers gain actionable skills for study design, bias assessment, and uncertainty quantification that directly improve decision-making in patient care and health policy.

Whether you’re advancing a research program or strengthening your analytical toolkit, this volume delivers a rigorous yet approachable path to modern inference with health-related markers. Crisp exposition, worked examples, and guidance for real datasets make it an indispensable reference for anyone working at the intersection of statistics and health science.

Add Modern Inference Based on Health-Related Markers, 1st Edition to your library today to sharpen your analytical practice and translate marker data into trustworthy, impactful results.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.