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2.3.2 Neyman’s Potential Outcome Notation

       2.3.3 Rubin’s Causal Model

       2.3.4 Pearl’s Causal Model

       2.4 Estimands

       2.5 Totality of Evidence: Replication, Exploratory, and Sensitivity Analyses

       2.6 Summary

       References

       Chapter 3: Data Examples and Simulations

       3.1 Introduction

       3.2 The REFLECTIONS Study

       3.3 The Lindner Study

       3.4 Simulations

       3.5 Analysis Data Set Examples

       3.5.1 Simulated REFLECTIONS Data

       3.5.2 Simulated PCI Data

       3.6 Summary

       References

       Chapter 4: The Propensity Score

       4.1 Introduction

       4.2 Estimate Propensity Score

       4.2.1 Selection of Covariates

       4.2.2 Address Missing Covariates Values in Estimating Propensity Score

       4.2.3 Selection of Propensity Score Estimation Model

       4.2.4 The Criteria of “Good” Propensity Score Estimate

       4.3 Example: Estimate Propensity Scores Using the Simulated REFLECTIONS Data

       4.3.1 A Priori Logistic Model

       4.3.2 Automatic Logistic Model Selection

       4.3.3 Boosted CART Model

       4.4 Summary

       References

       Chapter 5: Before You Analyze – Feasibility Assessment

       5.1 Introduction

       5.2 Best Practices for Assessing Feasibility: Common Support

       5.2.1 Walker’s Preference Score and Clinical Equipoise

       5.2.2 Standardized Differences in Means and Variance Ratios

       5.2.3 Tipton’s Index

       5.2.4 Proportion of Near Matches

       5.2.4 Proportion of Near Matches

       5.2.5 Trimming the Population

       5.3 Best Practices for Assessing Feasibility: Assessing Balance

       5.3.1 The Standardized Difference for Assessing Balance at the Individual Covariate Level

       5.3.2 The Prognostic Score for Assessing Balance

       5.4 Example: REFLECTIONS Data

       5.4.1 Feasibility Assessment Using the Reflections Data

       5.4.2 Balance Assessment Using the Reflections Data

       5.5 Summary

       References

       Chapter 6: Matching Methods for Estimating Causal Treatment Effects

       6.1 Introduction

       6.2 Distance Metrics

       6.2.1 Exact Distance Measure

       6.2.2 Mahalanobis Distance Measure

       6.2.3 Propensity Score Distance Measure

       6.2.4 Linear Propensity Score Distance Measure

       6.2.5 Some Considerations in Choosing Distance Measures

       6.3 Matching Constraints

       6.3.1 Calipers

       6.3.2 Matching With and Without Replacement

       6.3.3 Fixed Ratio Versus Variable Ratio Matching

       6.4 Matching Algorithms

       6.4.1 Nearest Neighbor Matching

       6.4.2 Optimal Matching

       6.4.3 Variable Ratio Matching

       6.4.4 Full Matching

       6.4.5 Discussion: Selecting the Matching Constraints and Algorithm

       6.5 Example: Matching Methods Applied to the Simulated REFLECTIONS Data

       6.5.1 Data Description

      

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