Timothy L. Lash – författare

Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow.

Selected as a Doody's Core Title for 2025!Now in a fully revised 4th Edition, Modern Epidemiology remains the gold standard text in this complex and evolving field, offering unparalleled, comprehensive coverage of the principles and methods of epidemiologic research. Featuring a new, full-color design, updated models, and a new format allowing space for margin notes, this edition continues to provide authoritative information on the methodologic issues crucial to the wide range of epidemiologic applications in public health and medicine. Reflects both the conceptual development of this evolving science and the increasing role that epidemiology plays in both public health and medicine. Features a new full-color design, new coverage of marginal structural models, new instrumental variable analysis, updated structural nested models, and more. Covers a broad range of concepts and methods, including epidemiologic measures of occurrence and effect, study designs, validity, precision, statistical interference, field methods, and causal diagrams. Includes data analysis topics such as Bayesian analysis, sensitivity analysis, and bias analysis, with an extensive overview of modern regression methods including logistic and survival regression, splines, hierarchical (multilevel) regression, propensity scores and other scoring methods, and g-estimation. Discusses special topics such as disease surveillance, ecologic studies, social epidemiology, infectious disease epidemiology, genetic and molecular epidemiology, nutritional epidemiology, environmental epidemiology, reproductive epidemiology, clinical epidemiology, and meta-analysis. Coauthored by three leading epidemiologists, with contributions from experts in a variety of epidemiologic sub-disciplines.

This textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the first edition but also further developing methods and adding new advanced methods.As computational power available to analysts has improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new edition features updated examples throughout and adds coverage addressing:Measurement error pertaining to continuous and polytomous variablesMethods surrounding person-time (rate) dataBias analysis using missing data, empirical (likelihood), and Bayes methodsA unique feature of this revision is its section on best practices for implementing, presenting, and interpreting bias analyses. Pedagogically, the text guides students and professionals through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and measurement errors, and subsequent sections extend these methods to probabilistic bias analysis, missing data methods, likelihood-based approaches, Bayesian methods, and best practices.