Applying Quantitative Bias Analysis to Epidemiologic Data - Matthew P. Fox

Applying Quantitative Bias Analysis to Epidemiologic Data

; Richard F. MacLehose ; Timothy L. Lash

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. Les mer
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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.
Forlag: Springer Nature Switzerland AG
Innbinding: Innbundet
Språk: Engelsk
Sider: 467
ISBN: 9783030826727
Utgave: 2. utg.
Format: 24 x 16 cm

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Part I: Introduction
1 Introduction and Objectives1 Introduction 1.2 Nonrandomized Epidemiologic Research 1.3 The Treatment of Uncertainty in Nonrandomized Research 1.4 Objective 1.5 Conclusion 2 A Guide to Implementing Quantitative Bias Analysis 2.1 Introduction 2.2 Reducing Error 2.3 Reducing Error by Design 2.4 Reducing Error in the Analysis 2.5 Quantifying Error 2.6 Evaluating the Potential Value of Quantitative Bias Analysis2.7 Planning for Bias Analysis 2.8 Creating a Data Collection Plan for Bias Analysis 2.9 Creating an Analytic Plan for a Bias Analysis 2.10 Bias Analysis Techniques 2.11 Introduction to Inference 2.12 Conclusion 3 Data Sources for Bias Analysis 3.1 Bias Parameters 3.2 Internal Data Sources 3.3 Selection Bias 3.4 Uncontrolled Confounder 3.5 Information Bias 3.6 Limitations of Internal Validation Studies 3.7 External Data Sources 3.8 Selection Bias 3.9 Uncontrolled Confounder 3.10 Information Bias 3.11 Summary
Part II: Preliminary Methods to Adjust for Systematic Errors 4 Selection Bias 4.1 Introduction 4.2 Definitions and Terms4.3 Motivation for Bias Analysis 4.4 Sources of Data 4.5 Simple Correction for Differential Initial Participation 4.6 Simple Correction for Differential Loss-to-Follow-up4.7 Sensitivity Analysis of the Bias Analysis 4.7 Signed Directed Acyclic Graphs to Estimate the Direction of Bias 5 Uncontrolled Confounders 5.1 Introduction 5.2 Definitions and Terms5.3 Motivation for Bias Analysis 5.4 Sources of Data5.5 Introduction to Simple Bias Analysis 5.6 Implementation of Simple Bias Analysis5.7 Sensitivity Analysis of the Bias Analysis 5.8 Uncontrolled Confounder in the Presence of Effect Modification 5.9 Polytomous Confounders 5.10 Bounding the Bias Limits of Uncontrolled Confounding5.10 Signed Directed Acyclic Graphs to Estimate the Direction of Bias5.11 Uncontrolled Confounding with Continuous Outcome, Exposure, or Confounder 6 Misclassification 6.1 Introduction 6.2 Definitions and Terms6.3 Motivation for Bias Analysis6.4 Sources of Data6.5 Calculating Classification Bias Parameters from Validation Data6.6 Exposure Misclassification for Dichotomous Exposures6.7 Exposure Misclassification for Polytomous Exposures6.8 Disease Misclassification 6.9 Covariate Misclassification 6.10 Dependent Misclassification6.11 Sensitivity Analysis of the Bias Analysis6.12 Adjusting Standard Errors for Corrections 7 Measurement Error for Continuous Variables7.1 Introduction7.2 Definition and Terms7.3 Motivation for Bias Analysis7.4 Exposure Measurement error7.5 Outcome Measurement error7.6 Covariate Measurement Error7.7 Correlated errors 8 Multiple Bias Modeling 8.1 Introduction 8.2 Order of Bias Analyses8.3 Multiple Bias Analysis, Simple Methods
Part III: Methods to Incorporate Systematic and Random Errors 9 Bias Analysis by Simulation for Summary Level Data9.1 Introduction 9.2 Probability Distributions 9.3 Correlated Distributions 9.4 Analytic Approach 9.5 Exposure Misclassification Implementation9.6 Exposure Measurement Error Implementation 9.7 Uncontrolled Confounding Implementation 9.8 Selection Bias Implementation 10 Bias Analysis by Simulation for Record Level Data10.1 Introduction 10.2 Analytic Approach 10.3 Exposure Misclassification Implementation10.4 Exposure Measurement Error Implementation 10.5 Uncontrolled Confounding Implementation 10.6 Selection Bias Implementation 11 Combining Systematic and Random Error11.1 Analytic approximation11.2 Resampling approximation11.3 Bootstrapping 12 Bias Analysis by Missing Data Methods12.1 Introduction 12.2 Analytic Approach 12.3 Exposure Misclassification Implementation12.4 Exposure Measurement Error Implementation 12.5 Uncontrolled Confounding Implementation 12.6 Selection Bias Implementation 12.7 Combining Systematic and Random Error 13 Bias Analysis by Empirical Methods13.1 Introduction 13.2 Analytic Approach 13.3 Exposure Misclassification Implementation 13.4 Exposure Measurement Error Implementation13.5 Uncontrolled Confounding Implementation 13.6 Selection Bias Implementation 13.7 Combining Systematic and Random Error 14 Bias Analysis by Bayesian Methods14.1 Introduction 14.2 Analytic Approach 14.3 Exposure Misclassification Implementation 14.4 Exposure Measurement Error Implementation 14.5 Uncontrolled Confounding Implementation 14.6 Selection Bias Implementation 14.7 Combining Systematic and Random Error 15 Multiple Bias Modeling15.1 Multiple Bias Analysis, Probabilistic Methods15.2 Multiple Bias Analysis, Missing Data Methods15.3 Multiple Bias Analysis, Empirical Methods15.4 Multiple Bias Analysis, Bayesian Methods
Part IV: Good Practices16 Good Practices for Quantitative Bias Analysis16.1 Selection of bias sources16.2 Selection of analytic strategies16.3 Selection of values to assign to bias parameters17 Presentation and Inference 17.1 Presentation of simple and multidimensional bias analyses17.2 Presentation of advanced bias analyses 17.3 Inference 17.4 Caveats and Cautions 18 References 19 Index
Timothy Lash, D.Sc., M.P.H., is professor in the Department of Epidemiology at the Rollins School of Public Health and honorary professor of cancer epidemiology in the Department of Clinical Epidemiology at Aarhus University in Aarhus, Denmark. Dr. Lash is also past-President of the Society for Epidemiologic Research (SER) for the 2014-2015 term. His research focuses on predictors of cancer recurrence, including molecular predictors of treatment effectiveness and late recurrence, and he also researches methods and applications of quantitative bias analysis.
Matthew Fox, D.Sc., M.P.H, is associate professor in the Center for Global Health & Development and in the Department of Epidemiology at Boston University. Before joining Boston University, he was a Peace Corps volunteer in the former Soviet Republic of Turkmenistan. Dr. Fox is currently funded through a K award from the National Institutes of Allergy and Infectious Diseases to work on ways to improve retention in HIV-care programs in South Africa from time of testing HIV-positive through long-term treatment. His research interests include treatment outcomes in HIV-treatment programs, infectious disease epidemiology, and epidemiological methods, including quantitative bias analysis.

Richard MacLehose, Ph.D., is associate professor in the Division of Epidemiology and Community Health at the University of Minnesota. Dr. MacLehose received his M.S. in epidemiology from the University of Washington and his Ph.D. in epidemiology from the University of North Carolina. His research interests include Bayesian statistics (including bias analysis), epidemiologic methods, applied biostatistics, and reproductive and environmental health.