Handbook of Statistical Methods for Precision Medicine

Eric B. Laber is the James B. Duke Distinguished Professor of Statistical Sciences and Biostatistics and Bioinformatics at Duke University. He is a fellow of the American Statistical Association and International Statistical Institute as well as the recipient of the Gottfried E. Noether Award, the Raymond J. Carroll Award, and the American Statistical Association Outstanding Application Award. Bibhas Chakraborty is an Associate Professor jointly appointed by the Duke-National University of Singapore Medical School (Duke-NUS) and the Department of Statistics and Data Science at the National University of Singapore. He also holds an adjunct faculty position with the Department of Biostatistics and Bioinformatics at Duke University. He is a 2011 recipient of the Calderone Research Prize for Junior Faculty from Columbia University, a 2017 recipient of the Young Statistical Scientist Award from the International Indian Statistical Association and is an Elected Member of the International Statistical Institute (ISI). Along with Dr. Erica E.M. Moodie, he co-authored the first textbook on dynamic treatment regimes (Springer, New York, 2013). Currently he serves as an Associate Editor for Biometrics. Erica E. M. Moodie is Professor of Biostatistics and Canada Research Chair in Statistical Methods for Precision Medicine at McGill University. She is the 2020 recipient of the CRM-SSC Prize in Statistics, is an Elected Member of the International Statistical Institute, and holds a chercheur de mérite career award from the Fonds de recherche du Québec-Santé. Dr Moodie is the Co-Editor of Biometrics and a Statistical Editor of Journal of Infectious Diseases. Tianxi Cai is the John Rock Professor of Population and Translational Data Science at Harvard Chan School of Public Health (HSPH) and a Professor of Biomedical Informatics at Harvard Medical School (HMS). Dr. Cai’s research includes statistical learning methods for efficient analysis of multi-institutional electronic health records data, real world evidence, and precision medicine using large scale genomic and phenomic data. Mark van der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor in Biostatistics and Statistics at the University of California, Berkeley. Mark research interests include censored data, causal inference, genomics and adaptive designs. Mark has led the development of Targeted Learning, including Super Learning and Targeted maximum likelihood estimation (TMLE). In 2005 Mark was awarded the Committee of Presidents of Statistical Societies (COPSS) Presidential Award. He also received the 2004 Spiegelman Award and 2005 van Dantzig Award. He is co-founder of the international Journal of Biostatistics and Journal of Causal Inference, and has authored various Springer books on Targeted Learning, Censored Data and Multiple Testing.

Preface Part 1: Study Design For Precision Medicine 1. Adaptive Designs for Precision Medicine: Fundamental Statistical Considerations 2. Small Sample, Sequential, Multiple Assignment, Randomized Trial Design and Analysis 3. Sequential Multiple Assignment Randomized Trial with Adaptive Randomization (SMART-AR) for Mobile Health Devices 4. Bayesian Dose-Finding in Two Treatment Cycles based on Efficacy and Toxicity 5. Agent-Based Modeling in Medical Research – Example in Health Economics 6. Thompson Sampling for mHealth and Precision Health Applications Part 2: Estimation of Optimal Treatment Strategies 7. Constructing and Evaluating Optimal Treatment Sequences: An Introductory Guide for Bayesians 8. Measurement Error in Adaptive Treatment Strategies 9. Nonparametric Heterogeneous Treatment Effect Estimation in Repeated Cross Sectional Designs 10. Semiparametric Doubly Robust Targeted Double Machine Learning: A Review 11. Adversarial Monte Carlo Meta-Learning of Conditional Average Treatment Effects 12. Personalized Policy Learning 13. Bandit Algorithms for Precision Medicine Part 3: Precision Medicine in High Dimensions 14. Tailoring Variable Selection and Ranking for Optimal Treatment Decisions 15. Selecting Optimal Subgroups for Treatment Using Many Covariates 16. Statistical Learning Methods for Estimating Optimal Individualized Treatment Rules from Observational Data 17. Polygenic Risk Prediction for Precision Prevention 18. Post-Selection Inference for Individualized Treatment Rules with Nonparametric Confounding Control Bibliography