Search results for ""Author Timothy L. Lash""
Springer Nature Switzerland AG Applying Quantitative Bias Analysis to Epidemiologic Data
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 variables Methods surrounding person-time (rate) data Bias analysis using missing data, empirical (likelihood), and Bayes methods A 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.
£56.99
Lippincott Williams and Wilkins Modern Epidemiology
Selected as a Doody's Core Title for 2022 and 2023! 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.
£92.00
Springer Nature Switzerland AG Applying Quantitative Bias Analysis to Epidemiologic Data
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 variables Methods surrounding person-time (rate) data Bias analysis using missing data, empirical (likelihood), and Bayes methods A 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.
£52.99
