Real World Health Care Data Analysis. Uwe Siebert
2.3.2 Neyman’s Potential Outcome Notation
2.5 Totality of Evidence: Replication, Exploratory, and Sensitivity Analyses
Chapter 3: Data Examples and Simulations
3.5 Analysis Data Set Examples
3.5.1 Simulated REFLECTIONS Data
Chapter 4: The Propensity Score
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
4.3.2 Automatic Logistic Model Selection
Chapter 5: Before You Analyze – Feasibility Assessment
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
5.2.4 Proportion of Near Matches
5.2.4 Proportion of Near Matches
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