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Leite, Walter

Practical propensity score methods using R Walter Leite - Los Ángeles (California, Estados Unidos) SAGE 2017 - xvii, 205 páginas

Overview of Propensity Score Analysis -- Learning Objectives -- Introduction -- Rubin's Causal Model -- Potential Outcomes -- Types of Treatment Effects -- Assumptions -- Campbell's Framework -- Propensity Scores -- Description of Example -- Steps of Propensity Score Analysis -- Data Preparation -- Propensity Score Estimation -- Propensity Score Method Implementation -- Covariate Balance Evaluation -- Treatment Effect Estimation -- Sensitivity Analysis -- Propensity Score Analysis With Complex Survey Data -- Resources for Learning R -- R Packages for Propensity Score Analysis -- Conclusion -- Study Questions -- Propensity Score Estimation -- Learning Objectives -- Introduction -- Description of Example -- Selection of Covariates -- Dealing With Missing Data -- Methods for Propensity Score Estimation -- Logistic Regression -- Recursive Partitioning Algorithms -- Generalized Boosted Modeling -- Evaluation of Common Support -- Conclusion -- Study Questions -- Propensity Score Weighting -- Learning Objectives -- Introduction -- Description of Example -- Calculation of Weights -- Covariate Balance Check -- Estimation of Treatment Effects With Propensity Score Weighting -- Propensity Score Weighting With Multiple Imputed Data Sets -- Doubly Robust Estimation of Treatment Effect With Propensity Score Weighting -- Sensitivity Analysis -- Conclusion -- Study Questions -- Propensity Score Stratification -- Learning Objectives -- Introduction -- Description of Example -- Propensity Score Estimation -- Propensity Score Stratification -- Covariate Balance Evaluation -- Estimation of Treatment Effects -- Marginal Mean Weighting Through Stratification -- Covariate Balance Evaluation -- Estimation of Treatment Effect -- Doubly Robust Estimation With MMWS -- Conclusion -- Study Questions -- Propensity Score Matching -- Learning Objectives -- Introduction -- Description of Example -- Propensity Score Estimation -- Propensity Score Matching Algorithms -- Greedy Matching -- Genetic Matching -- Optimal Matching -- Full Matching -- Evaluation of Covariate Balance -- Estimation of Treatment Effects -- Sensitivity Analysis -- Conclusion -- Study Questions -- Propensity Score Methods for Multiple Treatments -- Learning Objectives -- Introduction -- Description of Example -- Estimation of Generalized Propensity Scores With Multinomial Logistic Regression -- Estimation of Generalized Propensity Scores With Data Mining Methods -- Propensity Score Weighting for Multiple Treatments -- Covariate Balance With Weights From Multinomial Logistic Regression -- Covariate Balance With Weights From Generalized Boosted Modeling -- Marginal Mean Weighting Through Stratification for Multiple Treatment Versions -- Estimation of Treatment Effect of Multiple Treatments -- Conclusion -- Study Questions -- Propensity Score Methods for Continuous Treatment Doses -- Learning Objectives -- Introduction -- Description of Example -- Generalized Propensity Scores -- Dose Response Function -- Inverse Probability Weighting -- Estimation of the Average Treatment Effect -- Conclusion -- Study Questions -- Propensity Score Analysis With Structural Equation Models -- Learning Objectives -- Introduction -- Description of Example -- Latent Confounding Variables -- Estimation of Propensity Scores -- Propensity Score Methods -- Treatment Effect Estimation With Multiple-Group Structural Equation Models -- Treatment Effect Estimation With Multiple-Indicator and Multiple-Causes Models -- Conclusion -- Study Questions -- Weighting Methods for Time-Varying Treatments -- Learning Objectives -- Introduction -- Description of Example -- Inverse Probability of Treatment Weights -- Stabilized Inverse Probability of Treatment Weights -- Evaluation of Covariate Balance -- Estimation of Treatment Effects -- Weighted Regression With Cluster-Robust Standard Errors -- Generalized Estimating Equations -- Conclusion -- Study Questions -- Propensity Score Methods With Multilevel Data -- Learning Objectives -- Introduction -- Description of Example -- Estimation of Propensity Scores With Multilevel Data -- Multilevel Logistic Regression -- Logistic Regression With Fixed Cluster Effects -- Propensity Score Weighting -- Treatment Effect Estimation -- Conclusion -- Study Questions -- References. Machine generated contents note: ch. 1 1.1. 1.2. 1.2.1. 1.2.2. 7.2.3. 1.3. 1.4. 1.5. 1.6. 1.6.1. 1.6.2. 1.6.3. 1.6.4. 1.6.5. 1.6.6. 1.7. 1.8. 1.8.1. 1.9. ch. 2 2.1. 2.2. 2.3. 2.4. 2.5. 2.5.7. 2.5.2. 2.5.3. 2.6. 2.7. ch. 3 3.1. 3.2. 3.3. 3.4. 3.5. 3.6. 3.7. 3.8. 3.9. ch. 4 4.1. 4.2. 4.3. 4.4. 4.4.7. 4.4.2. 4.5. 4.5.7. 4.5.2. 4.5.3. 4.6. ch. 5 5.1. 5.2. 5.3. 5.4. 5.4.7. 5.4.2. 5.4.3. 5.4.4. 5.5. 5.6. 5.7. 5.8. ch. 6 6.1. 6.2. 6.3. 6.4. 6.5. 6.5.1. 6.5.2. 6.5.3. 6.6. 6.7. ch. 7 7.1. 7.2. 7.3. 7.3.7. 7.4. 7.4.1. 7.5. ch. 8 8.1. 8.2. 8.3. 8.4. 8.5. 8.6. 8.7. 8.8. ch. 9 9.1. 9.2. 9.3. 9.4. 9.5. 9.6. 9.6.1. 9.6.2. 9.7. ch. 10 10.1. 10.2. 10.3. 10.3.1. 10.3.2. 10.4. 10.5. 10.6.

9781452288888


Ciencias sociales--Investigación--Modelo estadístico
Investigación
Estadística
R (Computer program language)

519.5



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