(PDF) Applying Quantitative Bias Analysis to Epidemiologic Data 2nd Ed by Fox

This is the PDF eBook version for Applying Quantitative Bias Analysis to Epidemiologic Data 2nd Edition by Matthew P. Fox, Richard F. MacLehose, Timothy L. Lash

Table of Contents

Part I: Introduction

1 Introduction and Objectives
1 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 Analysis
2.7 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 Terms
4.3 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-up
4.7 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 Terms
5.3 Motivation for Bias Analysis
5.4 Sources of Data
5.5 Introduction to Simple Bias Analysis
5.6 Implementation of Simple Bias Analysis
5.7 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 Confounding
5.10 Signed Directed Acyclic Graphs to Estimate the Direction of Bias
5.11 Uncontrolled Confounding with Continuous Outcome, Exposure, or Confounder
6 Misclassification
6.1 Introduction
6.2 Definitions and Terms
6.3 Motivation for Bias Analysis
6.4 Sources of Data
6.5 Calculating Classification Bias Parameters from Validation Data
6.6 Exposure Misclassification for Dichotomous Exposures
6.7 Exposure Misclassification for Polytomous Exposures
6.8 Disease Misclassification
6.9 Covariate Misclassification
6.10 Dependent Misclassification
6.11 Sensitivity Analysis of the Bias Analysis
6.12 Adjusting Standard Errors for Corrections
7 Measurement Error for Continuous Variables
7.1 Introduction
7.2 Definition and Terms
7.3 Motivation for Bias Analysis
7.4 Exposure Measurement error
7.5 Outcome Measurement error
7.6 Covariate Measurement Error
7.7 Correlated errors
8 Multiple Bias Modeling
8.1 Introduction
8.2 Order of Bias Analyses
8.3 Multiple Bias Analysis, Simple Methods

Part III: Methods to Incorporate Systematic and Random Errors
9 Bias Analysis by Simulation for Summary Level Data
9.1 Introduction
9.2 Probability Distributions
9.3 Correlated Distributions
9.4 Analytic Approach
9.5 Exposure Misclassification Implementation
9.6 Exposure Measurement Error Implementation
9.7 Uncontrolled Confounding Implementation
9.8 Selection Bias Implementation
10 Bias Analysis by Simulation for Record Level Data
10.1 Introduction
10.2 Analytic Approach
10.3 Exposure Misclassification Implementation
10.4 Exposure Measurement Error Implementation
10.5 Uncontrolled Confounding Implementation
10.6 Selection Bias Implementation
11 Combining Systematic and Random Error
11.1 Analytic approximation
11.2 Resampling approximation
11.3 Bootstrapping
12 Bias Analysis by Missing Data Methods
12.1 Introduction
12.2 Analytic Approach
12.3 Exposure Misclassification Implementation
12.4 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 Methods
13.1 Introduction
13.2 Analytic Approach
13.3 Exposure Misclassification Implementation
13.4 Exposure Measurement Error Implementation
13.5 Uncontrolled Confounding Implementation
13.6 Selection Bias Implementation
13.7 Combining Systematic and Random Error
14 Bias Analysis by Bayesian Methods
14.1 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 Modeling
15.1 Multiple Bias Analysis, Probabilistic Methods
15.2 Multiple Bias Analysis, Missing Data Methods
15.3 Multiple Bias Analysis, Empirical Methods
15.4 Multiple Bias Analysis, Bayesian Methods

Part IV: Good Practices
16 Good Practices for Quantitative Bias Analysis
16.1 Selection of bias sources
16.2 Selection of analytic strategies
16.3 Selection of values to assign to bias parameters
17 Presentation and Inference
17.1 Presentation of simple and multidimensional bias analyses
17.2 Presentation of advanced bias analyses
17.3 Inference
17.4 Caveats and Cautions
18 References
19 Index