책 이미지

책 정보
· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9783030097363
· 쪽수 : 640쪽
· 출판일 : 2018-12-15
목차
Part I:? Introductory Chapters
1. ?The Statistical Estimation Problem in Complex Longitudinal Data
- Data Science and Statistical Estimation
- Roadmap for Causal Effect Estimation
- Role of Targeted Learning in Data Science
- Observed Data
- Caussal Model and Causal target Quantity
- Statistical Model
- Statistical Target Parameter
- Statistical Estimation Problem
- Structural Causal Models
- Causal Graphs / DAGs
- Nonparametric Structural Equation Models
3.? Super Learner for Longitudinal Problems
- Ensemble Learning
- Sequential Regression
4.? Longitudinal Targeted Maximum Likelihood Estimation (LTMLE)
- Step-by-Step Demonstration of LTMLE
- Statistical Properties
- Theoretical Background
6.? Why LTMLE?
- Landscape of Other Estimators
- Comparison of Statistical Properties
?
Part II:? Additional Core Topics
7.? One-Step TMLE
- General Framework
- Theoretical Results
- Demonstration for Effect Among the Treated
- Simulation Studies
9.? Online Targeted Learning
- Batched Streaming Data
- Online and One-Step Estimator
- Theoretical Considerations
10.? Networks
- General Statistical Framework
- Causal Model for Network Da
11. Application to Networks
- Differing Network Structures
- Realistic Network Examples (e.g., effect of vaccination)
- R Package Implementation of TMLE
12. Targeted Estimation of the Nuisance Parameter
- Asymptotic Linearity
- IPW
- TMLE
13. Sensitivity Analyses
- General Nonparametric Approach to Sensitivity Analysis
- Measurement Error
- Unmeasured Confounding
- Informative Missingness of the Outcome
- FDA Meta-Analysis
?
Part III:? Randomized Trials
14. Community Randomized Trials for Small Samples
- Introduction of SEARCH Community Rando
15. Sample Average Treatment Effect in a CRT
- Introduction of the Parameter
- Effect for the Observed Communities
- Inference
16. Application to Clinical Trial Survival Data
- Introduction of the Survival Parameter
- Censoring
- Treatment-Specific Survival Function
- Effect of Pandora Streaming on Music Sales
- Application of TMLE
18. Causal Effect Transported Across Sites
- Intent-to-Treat ATE
- Complier ATE
- Incomplete Data
- Moving to Opportunity Trial
?
Part IV:? Observational Longitudinal Data
19. Super Learning in the ICU
- ICU Prediction Problem
- Super Learning Algorithm
- Defining Stochastic Interventions
- Dependence on True Treatment Mechanisms
- Continuous Exposure
- Air Pollution Data Example
21. Stochastic Multiple-Time-Point Interventions on Monitoring and Treatment
- Defining Stochastic Interventions for Multiple-Time Points
- Introduction of Monitoring Problem
- Non-direct Effect Assumption of Monitoring
- Dynamic Treatment
- Diabetes Data Example
22. Collaborative LTMLE
- Collaborative LTMLE Framework
- Breastfeeding Data Example
?
Part V:? Optimal Dynamic Regimes
23. Targeted Adaptive Designs Learning the Optimal Dynamic Treatment
- Group-Sequential Adaptive Designs
- Multiple Bandit Problem
- Treatment Allocation Learning from Past Data
- Mean Outcome Under the Optimal Treatment
- Martingale Theory
- Inference
24. Targeted Learning of the Optimal Dynamic Treatment
- Super Learning for Discovering the Optimal Dynamic rule
- Different Loss Functions
- TMLE for the Counterfactual Mean
- Statistical Inference for? the Mean Outcome Under the Optimal Rule
25. Optimal Dynamic Treatments Under Resource Constraints
- Constrained Optimal Dynamic Treatment
- Super Learning of the Constrained Optimal Dynamic Regime
- TMLE of the Counterfactual Mean Under the Constrained
?
Part VI:? Computing
26. ltmle() for R
- Introduction to the ltmle() R Package
- Demonstration of the ltmle() R Package
27. Scaled Super Learner for R
Introduction to the H2O Environment
- R Package
- Subsemble
28. Scaling CTMLE for Julia
- Scaling Computing of CTMLE in Julia
- Pharmacoepidemiology Example
?
Part VII:? Special Topics
29. Data-Adaptive Target Parameters
- Definition of Parameter
- Examples of Data-Adaptive Target Parameters as Arise in Data Mining
- Estimators of the Data-Adaptive Target Parameters Using Sample Splitting
- Estimators of the Data-Adaptive Target Parameters Without Sample Splitting
- Cross-Validated TMLE of the Data-Adap
30. Double Robust Inference for LTMLE
- The Challenge of Double Robust Inference for Double Robust Estimators
- Higher-Order Pathwise Differentiable Target Parameters
- Higher-Order TMLE
- Kth Order Remainder
- Parameters Not Second-Order Pathwise Differentiable
- Second-Order U Statistics
- Approximate Second-Order Influence Function
- Approximate Second-Order TMLE
31. Higher-Order TMLE
?
Appendices
A.? Online Targeted Learning Theory
B.? Computerization of the Calculation of Efficient Influence Curve
D.? TMLE for High Dimensional Linear Regression
E.? TMLE of Causal Effect Based on Observing a Single Time Series