Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Hardcover, 2018)

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 2. Longitudinal Causal Models 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 scalable inference="" for="" big="" data 5. Understanding 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 8. One-Step TMLE for the Effect Among the Treated 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 ta Counterfactual Mean Under Stochastic Intervention on the Network Development of TMLE for Networks Inference 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 mized Trial Adaptive Pair Matching Data-Adaptive Selection of Covariates for Small Samples TMLE Using Super Learning for Small Samples Inference 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 17. Application to Pandora Music Data 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