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Practical propensity score methods using R

por Leite, Walter

Publicado por : SAGE (Los Ángeles (California, Estados Unidos)) Detalles físicos: xvii, 205 páginas ISBN:9781452288888.

Tema(s): Ciencias sociales -- Investigación -- Modelo estadístico | Investigación | Estadística | R (Computer program language)

Año : 2017

Existencias ( 1 )
Notas de título ( 1 )

Tipo de ítem	Ubicación actual	Colección	Signatura	Copia número	Estado	Fecha de vencimiento	Código de barras	Reserva de ítems
Libro	Claustro 4to piso	Libro	519.5 L533pp (Navegar estantería)	Ej.1	Disponible		100155636

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Previo								Siguiente
Previo	519.5 K73 Introducción a la estadística matemática principios y métodos	519.5 L17 Introducción a la teoría de probabilidades e inferencia estadística	519.5 L523f Fisher, Neyman, and the creation of clasical statistics	519.5 L533pp Practical propensity score methods using R	519.5 L791in Introducción a la estadística matemática	519.5 M126s Statistics	519.5 M337b Bayesian Essentials with R	Siguiente

Machine generated contents note: 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.