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Practical propensity score methods using R

Author: Walter Leite
Publisher: Los Angeles, California : Sage, [2017] ©2017
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
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.--
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Document Type: Book
All Authors / Contributors: Walter Leite
ISBN: 9781452288888 1452288887
OCLC Number: 951753910
Description: xvii, 205 pages : illustrations ; 23 cm
Contents: ch. 1 Overview of Propensity Score Analysis --
Learning Objectives --
1.1. Introduction --
1.2. Rubin's Causal Model --
1.2.1. Potential Outcomes --
1.2.2. Types of Treatment Effects --
7.2.3. Assumptions --
1.3. Campbell's Framework --
1.4. Propensity Scores --
1.5. Description of Example --
1.6. Steps of Propensity Score Analysis --
1.6.1. Data Preparation --
1.6.2. Propensity Score Estimation --
1.6.3. Propensity Score Method Implementation --
1.6.4. Covariate Balance Evaluation --
1.6.5. Treatment Effect Estimation --
1.6.6. Sensitivity Analysis --
1.7. Propensity Score Analysis With Complex Survey Data --
1.8. Resources for Learning R --
1.8.1. R Packages for Propensity Score Analysis --
1.9. Conclusion --
Study Questions --
ch. 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 --
2.5.7. Logistic Regression --
2.5.2. Recursive Partitioning Algorithms --
2.5.3. Generalized Boosted Modeling --
2.6. Evaluation of Common Support --
2.7. Conclusion --
Study Questions --
ch. 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 --
ch. 4 Propensity Score Stratification --
Learning Objectives --
4.1. Introduction --
4.2. Description of Example --
4.3. Propensity Score Estimation --
4.4. Propensity Score Stratification --
4.4.7. Covariate Balance Evaluation --
4.4.2. Estimation of Treatment Effects --
4.5. Marginal Mean Weighting Through Stratification --
4.5.7. Covariate Balance Evaluation --
4.5.2. Estimation of Treatment Effect --
4.5.3. Doubly Robust Estimation With MMWS --
4.6. Conclusion --
Study Questions --
ch. 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 --
5.4.7. Greedy Matching --
5.4.2. Genetic Matching --
5.4.3. Optimal Matching --
5.4.4. Full Matching --
5.5. Evaluation of Covariate Balance --
5.6. Estimation of Treatment Effects --
5.7. Sensitivity Analysis --
5.8. Conclusion --
Study Questions --
ch. 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 --
6.5.1. Covariate Balance With Weights From Multinomial Logistic Regression --
6.5.2. Covariate Balance With Weights From Generalized Boosted Modeling --
6.5.3. Marginal Mean Weighting Through Stratification for Multiple Treatment Versions --
6.6. Estimation of Treatment Effect of Multiple Treatments --
6.7. Conclusion --
Study Questions --
ch. 7 Propensity Score Methods for Continuous Treatment Doses --
Learning Objectives --
7.1. Introduction --
7.2. Description of Example --
7.3. Generalized Propensity Scores --
7.3.7. Dose Response Function --
7.4. Inverse Probability Weighting --
7.4.1. Estimation of the Average Treatment Effect --
7.5. Conclusion --
Study Questions --
ch. 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 --
ch. 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 --
9.6.1. Weighted Regression With Cluster-Robust Standard Errors --
9.6.2. Generalized Estimating Equations --
9.7. Conclusion --
Study Questions --
ch. 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 --
10.3.1. Multilevel Logistic Regression --
10.3.2. Logistic Regression With Fixed Cluster Effects --
10.4. Propensity Score Weighting --
10.5. Treatment Effect Estimation --
10.6. Conclusion --
Study Questions --
References.
Responsibility: Walter Leite, University of Florida.

Abstract:

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.  Read more...

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In general, the book is well-crafted and focuses on practical implementation of propensity score methods featuring the free software R. Even though there is room for improvement that could be Read more...

 
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