Practical propensity score methods using R
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書誌事項
Practical propensity score methods using R
SAGE, 2017
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注記
Includes bibliographical references (pages 191-200) and index
内容説明・目次
内容説明
This practical book uses a step-by-step analysis of realistic examples to help students understand the theory and code for implementing propensity score analysis with the R statistical language. With a comparison of both well-established and cutting-edge propensity score methods, the text highlights where solid guidelines exist to support best practices and where there is scarcity of research. Readers will find that this scaffolded approach to R and the book's free online resources help them apply the text's concepts to the analysis of their own data.
目次
Preface
Acknowledgments
About the Author
Chapter 1. Overview of Propensity Score Analysis
Learning Objectives
1.1 Introduction
1.2 Rubin's Causal Model
1.3 Campbell's Framework
1.4 Propensity Scores
1.5 Description of Example
1.6 Steps of Propensity Score Analysis
1.7 Propensity Score Analysis With Complex Survey Data
1.8 Resources for Learning R
1.9 Conclusion
Study Questions
Chapter 2. Propensity Score Estimation
Learning Objectives
2.1 Introduction
2.2 Description of Example
2.3 Selection of Covariates
2.4 Dealing With Missing Data
2.5 Methods for Propensity Score Estimation
2.6 Evaluation of Common Support
2.7 Conclusion
Study Questions
Chapter 3. Propensity Score Weighting
Learning Objectives
3.1 Introduction
3.2 Description of Example
3.3 Calculation of Weights
3.4 Covariate Balance Check
3.5 Estimation of Treatment Effects With Propensity Score Weighting
3.6 Propensity Score Weighting With Multiple Imputed Data Sets
3.7 Doubly Robust Estimation of Treatment Effect With Propensity Score Weighting
3.8 Sensitivity Analysis
3.9 Conclusion
Study Questions
Chapter 4. Propensity Score Stratification
Learning Objectives
4.1 Introduction
4.2 Description of Example
4.3 Propensity Score Estimation
4.4 Propensity Score Stratification
4.5 Marginal Mean Weighting Through Stratification
4.6 Conclusion
Study Questions
Chapter 5. Propensity Score Matching
Learning Objectives
5.1 Introduction
5.2 Description of Example
5.3 Propensity Score Estimation
5.4 Propensity Score Matching Algorithms
5.5 Evaluation of Covariate Balance
5.6 Estimation of Treatment Effects
5.7 Sensitivity Analysis
5.8 Conclusion
Study Questions
Chapter 6. Propensity Score Methods for Multiple Treatments
Learning Objectives
6.1 Introduction
6.2 Description of Example
6.3 Estimation of Generalized Propensity Scores With Multinomial Logistic Regression
6.4 Estimation of Generalized Propensity Scores With Data Mining Methods
6.5 Propensity Score Weighting for Multiple Treatments
6.6 Estimation of Treatment Effect of Multiple Treatments
6.7 Conclusion
Study Questions
Chapter 7. Propensity Score Methods for Continuous Treatment Doses
Learning Objectives
7.1 Introduction
7.2 Description of Example
7.3 Generalized Propensity Scores
7.4 Inverse Probability Weighting
7.5 Conclusion
Study Questions
Chapter 8. Propensity Score Analysis With Structural Equation Models
Learning Objectives
8.1 Introduction
8.2 Description of Example
8.3 Latent Confounding Variables
8.4 Estimation of Propensity Scores
8.5 Propensity Score Methods
8.6 Treatment Effect Estimation With Multiple-Group Structural Equation Models
8.7 Treatment Effect Estimation With Multiple-Indicator and Multiple-Causes Models
8.8 Conclusion
Study Questions
Chapter 9. Weighting Methods for Time-Varying Treatments
Learning Objectives
9.1 Introduction
9.2 Description of Example
9.3 Inverse Probability of Treatment Weights
9.4 Stabilized Inverse Probability of Treatment Weights
9.5 Evaluation of Covariate Balance
9.6 Estimation of Treatment Effects
9.7 Conclusion
Study Questions
Chapter 10. Propensity Score Methods With Multilevel Data
Learning Objectives
10.1 Introduction
10.2 Description of Example
10.3 Estimation of Propensity Scores With Multilevel Data
10.4 Propensity Score Weighting
10.5 Treatment Effect Estimation
10.6 Conclusion
Study Questions
References
Index
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