Explanation in Causal Inference Methods for Mediation and Interaction 1st edition by Tyler VanderWeele – Ebook PDF Instant Download/Delivery: 0199325871, 978-0199325870
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ISBN 10: 0199325871
ISBN 13: 978-0199325870
Author: Tyler VanderWeele
The book provides an accessible but comprehensive overview of methods for mediation and interaction. There has been considerable and rapid methodological development on mediation and moderation/interaction analysis within the causal-inference literature over the last ten years. Much of this material appears in a variety of specialized journals, and some of the papers are quite technical. There has also been considerable interest in these developments from empirical researchers in the social and biomedical sciences. However, much of the material is not currently in a format that is accessible to them. The book closes these gaps by providing an accessible, comprehensive, book-length coverage of mediation.
The book begins with a comprehensive introduction to mediation analysis, including chapters on concepts for mediation, regression-based methods, sensitivity analysis, time-to-event outcomes, methods for multiple mediators, methods for time-varying mediation and longitudinal data, and relations between mediation and other concepts involving intermediates such as surrogates, principal stratification, instrumental variables, and Mendelian randomization. The second part of the book concerns interaction or “moderation,” including concepts for interaction, statistical interaction, confounding and interaction, mechanistic interaction, bias analysis for interaction, interaction in genetic studies, and power and sample-size calculation for interaction. The final part of the book provides comprehensive discussion about the relationships between mediation and interaction and unites these concepts within a single framework. This final part also provides an introduction to spillover effects or social interaction, concluding with a discussion of social-network analyses.
The book is written to be accessible to anyone with a basic knowledge of statistics. Comprehensive appendices provide more technical details for the interested reader. Applied empirical examples from a variety of fields are given throughout. Software implementation in SAS, Stata, SPSS, and R is provided. The book should be accessible to students and researchers who have completed a first-year graduate sequence in quantitative methods in one of the social- or biomedical-sciences disciplines. The book will only presuppose familiarity with linear and logistic regression, and could potentially be used as an advanced undergraduate book as well.
Explanation in Causal Inference Methods for Mediation and Interaction 1st Table of contents:
Part 1 Mediation Analysis
1 Explanation and Mechanism
1.1 Causal Inference and Explanation
1.2 Forms of Explanation and Types of Mechanisms
1.3 Motivations for Assessing Mediation, Interaction, and Interference
1.4 Organization of this Book
2 Mediation: Introduction and Regression-Based Approaches
2.1 Classic Regression Approach to Mediation Analysis
2.2 Counterfactual Approach to Mediation Analysis: Continuous Outcomes
2.3 Assumptions about Confounding
2.4 Binary and Count Outcomes
2.5 Binary Mediators
2.6 Comparison of Approaches: Product-of-Coefficient and Difference Methods
2.7 Description of the SAS Macro
2.8 Description of the SPSS Macro
2.9 Description of the Stata Macro
2.10 Hypothetical Example with Output
2.11 Empirical Example in Genetic Epidemiology
2.12 When to Include an Exposure–Mediator Interaction
2.13 Proportion Mediated
2.14 Proportion Eliminated
2.15 Study Design and Mediation Analysis
2.16 Counterfactual Notation for Natural Direct and Indirect Effects
2.17 An Alternative Regression-Based Estimation Approach Using Simulations
2.18 Code for the Simulation-Based Approach in R
2.19 Discussion
3 Sensitivity Analysis for Mediation
3.1 Sensitivity Analysis for Unmeasured Confounding for Total Effects
3.2 Sensitivity Analysis for Unmeasured Confounding for Controlled Direct Effects
3.3 Sensitivity Analysis for Unmeasured Confounding for Natural Direct and Indirect Effects
3.4 Sensitivity Analysis Using Two Trials
3.5 Sensitivity Analysis for Direct and Indirect Effects in the Presence of Measurement Error
3.6 Discussion
4 Mediation Analysis with Survival Data
4.1 Earlier Literature on Mediation Analysis with Survival Models
4.2 Mediation Analysis with an Accelerated Failure Time Model
4.3 Mediation Analysis with a Proportional Hazards Model
4.4 Mediation with an Additive Hazard Model
4.5 A Weighting Approach to Direct and Indirect Effects with Survival Outcomes
4.6 Sensitivity Analysis with Survival Data
4.7 Discussion
5 Multiple Mediators
5.1 Regression-Based Approaches to Multiple Mediators
5.2 A Weighting Approach to Multiple Mediators
5.3 Controlled Direct Effects and Exposure-Induced Confounding
5.4 Effect Decomposition with Exposure-Induced Confounding
5.5 Path-Specific Effects
5.6 Sensitivity Analysis for Exposure-Induced Confounding
5.7 Discussion
6 Mediation Analysis with Time-Varying Exposures and Mediators
6.1 Notation and Definitions
6.2 Controlled Direct Effects with Time-Varying Exposures and Mediators
6.3 Natural Direct and Indirect Effects and their Randomized Interventional Analogues with Time-Vary
6.4 Counterfactual Analysis of MacKinnon’s Three-Wave Mediation Model
6.5 Discussion
7 Selected Topics in Mediation Analysis
7.1 Other Estimation Approaches
7.2 Ill-Defined Mediators and Multiple Versions of the Mediator
7.3 Controversies Over Assumptions and Alternative Interpretations of Effects
7.4 Direct and Indirect Effects in Health Disparities Research
7.5 Rubin’s Seemingly Problematic Examples
7.6 A Three-Way Decomposition into Direct, Indirect, and Interactive Effects
7.7 Alternative Identification Strategies Using Confounding Control
7.8 Identification Using Baseline Covariates that Interact with Exposure
7.9 Power and Sample Size Calculations for Mediation Analysis
7.10 Discussion
8 Other Topics Related to Intermediates
8.1 Principal Stratification
8.2 Surrogate Outcomes
8.3 Instrumental Variables
8.4 Mendelian Randomization
8.5 Discussion
Part 2 Interaction Analysis
9 An Introduction to Interaction Analysis
9.1 Measures of Interaction and Scale of Interaction
9.2 Statistical Interactions and Statistical Inference
9.3 Inference for Additive Interaction
9.4 SAS and Stata Code for Additive Interaction from Logistic Regression
9.5 Additive Versus Multiplicative Interaction
9.6 Confounding and the Interpretation of Interaction: Interaction Versus Effect Heterogeneity
9.7 Presenting Interaction Analyses
9.8 Synergism and Mechanistic Interaction
9.9 Interactions for Continuous Outcomes and Time-to-Event Outcomes
9.10 Identifying Subgroups to Target Treatment
9.11 Qualitative Interaction
9.12 Attributing Effects to Interactions
9.13 Discussion
10 Mechanistic Interaction
10.1 Sufficient Causes and Synergism
10.2 Statistical Interaction with No Mechanistic Interaction
10.3 Empirical Tests for Sufficient Cause Synergism
10.4 Sufficient Cause Interaction and Statistical Interactions
10.5 “Epistatic” or Singular Interactions
10.6 Extensions to Ordinal Exposures
10.7 Extensions to Three or More Exposures
10.8 Other Extensions
10.9 Antagonism
10.10 Limits of Inference Concerning Biology
10.11 Discussion
11 Bias Analysis for Interactions
11.1 Sensitivity Analysis and Robustness for Additive Interaction
11.2 Sensitivity Analysis and Robustness for Multiplicative Interaction
11.3 Sensitivity Analysis for the Relative Excess Risk Due to Interaction
11.4 Measurement Error and Additive Interaction
11.5 Measurement Error and Multiplicative Interaction
11.6 Discussion
12 Interaction in Genetics: Independence and Boosting Power
12.1 Case-Only Estimators of Interaction
12.2 Joint Tests for Interactions and Main Effects
12.3 Multiple Testing
12.4 Discussion
13 Power and Sample-Size Calculations for Interaction Analysis
13.1 Power and Sample-Size Calculations for Interaction for Continuous Outcomes
13.2 Power and Sample-Size Calculations for Binary Outcomes: Multiplicative Interaction
13.3 Power and Sample Size Calculations for Binary Outcomes: Additive Interaction
13.4 Power and Sample Size Calculations for Binary Outcomes: Mechanistic Interaction
13.5 Excel Spreadsheets for Sample-Size and Power Calculations for Additive and Multiplicative Inter
13.6 Discussion
Part 3 Synthesis and Spillover Effects
14 A Unification of Mediation and Interaction
14.1 Notation and Definitions
14.2 Fourfold Decomposition: The Unification of Mediation and Interaction
14.3 Identification of the Effects
14.4 Relation to Statistical Models
14.5 Binary Outcomes and the Ratio Scale
14.6 Illustration in Genetic Epidemiology
14.7 Relation to Mediation Decompositions
14.8 Relation to Interaction Decompositions
14.9 SAS Code for the Four-Way Decomposition
14.10 Discussion
15 Social Interactions and Spillover Effects
15.1 Notation and Definitions for Spillover Effects
15.2 Basic Spillover and Individual/Direct Effects
15.3 Assessing “Infectiousness” Effects
15.4 Contagion versus Infectiousness Effects
15.5 Tests for Specific Forms of Interference Using Causal Interactions
15.6 Inferential Challenges with Many Individuals per Cluster
15.7 Spillover Effects and Observational Data
15.8 Spillover Effects and Social Networks
15.9 Discussion
16 Mediation and Interaction: Future and Context
16.1 The Present State of Methods and Future Methodological Development
16.2 Philosophical Questions
Appendix Technical Details and Proofs
References
Index
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Tags: Tyler VanderWeele, Causal Inference, Mediation and Interaction