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
  

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About the Author:

Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. His applied research involves applications in HIV and safety analysis, among others. He has published over 250 journal articles, 4 books, and one handbook on big data. Dr. van der Laan is also co-founder and co-editor of the International Journal of Biostatistics and the Journal of Causal Inference and associate editor of a variety of journals. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics or statistics.

Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose's methodological research focuses on nonparametric machine learning for causal inference and prediction. She has made major contributions to the development and application of targeted learning estimators, as well as adaptations to super learning for varied scientific problems. Within health policy, Dr. Rose works on comparative effectiveness research, health program impact evaluation, and computational health economics. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.