Applying Quantitative Bias Analysis to Epidemiologic Data [electronic resource] /
Lash, Timothy L.author.
Fox, Matthew P.author.
Fink, Aliza K.author.
This text provides the first-ever compilation of bias analysis methods for use with epidemiologic data. It guides the reader 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 classification errors. Subsequent chapters extend these methods to multidimensional bias analysis, probabilistic bias analysis, and multiple bias analysis. The text concludes with a chapter on presentation and interpretation of bias analysis results. Although techniques for bias analysis have been available for decades, these methods are considered difficult to implement. This text not only gathers the methods into one cohesive and organized presentation, it also explains the methods in a consistent fashion and provides customizable spreadsheets to implement the solutions. By downloading the spreadsheets (available at links provided in the text), readers can follow the examples in the text and then modify the spreadsheet to complete their own bias analyses. Readers without experience using quantitative bias analysis will be able to design, implement, and understand bias analyses that address the major threats to the validity of epidemiologic research. More experienced analysts will value the compilation of bias analysis methods and links to software tools that facilitate their projects. Timothy L. Lash is an Associate Professor of Epidemiology and Matthew P. Fox is an Assistant Professor in the Center for International Health and Development, both at the Boston University School of Public Health. Aliza K. Fink is a Project Manager at Macro International in Bethesda, Maryland. Together they have organized and presented many day-long workshops on the methods of quantitative bias analysis. In addition, they have collaborated on many papers that developed methods of quantitative bias analysis or used the methods in the data analysis.
Description based upon print version of record.
Includes bibliographical references and index.
Introduction, Objectives, and an Alternative -- A Guide to Implementing Quantitative Bias Analysis -- Data Sources for Bias Analysis -- Selection Bias -- Unmeasured and Unknown Confounders -- Misclassification -- Multidimensional Bias Analysis -- Probabilistic Bias Analysis -- Multiple Bias Modeling -- Presentation and Inference.
This text provides the first-ever compilation of bias analysis methods for use with epidemiologic data. It guides the reader 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 classification errors. Subsequent chapters extend these methods to multidimensional bias analysis, probabilistic bias analysis, and multiple bias analysis. The text concludes with a chapter on presentation and interpretation of bias analysis results. Although techniques for bias analysis have been available for decades, these methods are considered difficult to implement. This text not only gathers the methods into one cohesive and organized presentation, it also explains the methods in a consistent fashion and provides customizable spreadsheets to implement the solutions. By downloading the spreadsheets (available at links provided in the text), readers can follow the examples in the text and then modify the spreadsheet to complete their own bias analyses. Readers without experience using quantitative bias analysis will be able to design, implement, and understand bias analyses that address the major threats to the validity of epidemiologic research. More experienced analysts will value the compilation of bias analysis methods and links to software tools that facilitate their projects. Timothy L. Lash is an Associate Professor of Epidemiology and Matthew P. Fox is an Assistant Professor in the Center for International Health and Development, both at the Boston University School of Public Health. Aliza K. Fink is a Project Manager at Macro International in Bethesda, Maryland. Together they have organized and presented many day-long workshops on the methods of quantitative bias analysis. In addition, they have collaborated on many papers that developed methods of quantitative bias analysis or used the methods in the data analysis.
Public health.
Health informatics.
Epidemiology.
Statistics .
Social sciences.
Infectious diseases.
Public Health.
Health Informatics.
Epidemiology.
Statistics for Life Sciences, Medicine, Health Sciences.
Methodology of the Social Sciences.
Infectious Diseases.
Statistics for Biology and Health