Applying Quantitative Bias Analysis to Epidemiologic Data (Statistics for Biology and Health) 2nd Edition
Enhance the credibility of your epidemiologic and public health research with Applying Quantitative Bias Analysis to Epidemiologic Data, the fully updated 2nd Edition (2021/2022) by Timothy L. Lash, Matthew P. Fox, and Richard F. MacLehose—part of Springer’s respected Statistics for Biology and Health series. Now readily available at Books Hub PK, this textbook is the essential resource for epidemiologists, biostatisticians, and public health professionals aiming to evaluate and correct for systematic errors in observational studies.
🔍 Why This Book Matters
Bias—whether from selection, confounding, or misclassification—can undermine the validity of epidemiologic findings unless formally quantified and adjusted. This is the first comprehensive text that brings quantitative bias analysis (QBA) into an accessible and implementable framework. Across 15 detailed chapters, the authors guide readers through:
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Planning and designing validation studies
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Correcting for selection bias, uncontrolled confounding, and measurement error
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Advanced methods including probabilistic bias analysis, Bayesian techniques, and missing data corrections
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Practical advice on presentation, interpretation, and best practices for reporting bias analyses.
📚 Features & Content Highlights
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467 pages of structured instruction with real-world examples, downloadable spreadsheets, and SAS code to customize analyses.
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In-depth chapters addressing rate-based data, continuous and polytomous measurement error, and integrating empirical likelihood methods into bias correction.
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A new section devoted to designing bias validation studies, collecting external validity data, and planning bias and confounding corrections strategically.
