21 citations · 22 across the 12 of their papers we have counts for
13 papers
Backward Bayesian Outcome Weighted Learning
Emmanuel M. Rockwell, Michael R. Kosorok, Nikki L. B. Freeman
A central objective of precision medicine is learning optimal dynamic treatment regimes (DTRs) from data. Classification-based methods, like outcome weighted learning (OWL) for sin…
Bayesian Mediation Analysis for Individualized Treatment Rules
Emmanuel M. Rockwell, Patrick J. Smith, Michael R. Kosorok +1
The value of an individualized treatment rule (ITR), defined as the expected outcome under treatment assignment according to the rule, is useful for assessing average clinical bene…
The Fidelity and Feedback Traps: The Case for Health Digital Twins as Modular Evolving Causal Systems
Nikki L. B. Freeman, Yating Zou, Kyungbok Lee +3
Digital twins for health may be used to compare treatments, project patient trajectories, and support clinical decisions. While related to mechanical digital twins, those initially…
A Review of Methods and Practices for Missing Data in Sequential Multiple Assignment Randomized Trials (SMARTs): An Ancillary Study of a Scoping Review
Nikki L. B. Freeman, Chenyao Yu, Margaret Hoch +5
Background: Missing data poses an acute threat to sequential multiple assignment randomized trial (SMART) analyses because of the sequential treatment structure and response-depend…
Distributional Random Forests for Complex Survey Designs
Yating Zou, Marcos Matabuena, Michael R. Kosorok
We study estimation of the conditional law and continuous measurable maps of it when takes values in a locally compact Polish space (e.g., $\mathbb…
Confidence Regions for Multiple Outcomes, Effect Modifiers, and Other Multiple Comparisons
Paul N Zivich, Stephen R Cole, Noah Greifer +3
In epidemiology, some have argued that multiple comparison corrections are not necessary as there is rarely interest in the universal null hypothesis. From a parameter estimation p…
Linear Regression Using Principal Components from General Hilbert-Space-Valued Covariates
Xinyi Li, Margaret Hoch, Michael R. Kosorok
We introduce Adaptive Subspace PCA (AS-PCA), a framework for principal component analysis of random elements in a general separable Hilbert space. AS-PCA projects the covariance op…
Statistical Design and Rationale of the Biomarkers for Evaluating Spine Treatments (BEST) Trial
John Sperger, Kelley M. Kidwell, Matthew C. Mauck +16
Chronic low back pain (cLBP) is a prevalent condition with profound impacts on functioning and quality of life. While multiple evidence-based treatments exist, they all have modest…
Using Statistical Precision Medicine to Identify Optimal Treatments in a Heart Failure Setting
Arti Virkud, Jessie K. Edwards, Michele Jonsson Funk +4
Identifying optimal medical treatments to improve survival has long been a critical goal of pharmacoepidemiology. Traditionally, we use an average treatment effect measure to compa…
Off-Policy Reinforcement Learning with High Dimensional Reward
Dong Neuck Lee, Michael R. Kosorok
Conventional off-policy reinforcement learning (RL) focuses on maximizing the expected return of scalar rewards. Distributional RL (DRL), in contrast, studies the distribution of r…
Uncertainty quantification for intervals
Carlos García Meixide, Michael R. Kosorok, Marcos Matabuena
Data following an interval structure are increasingly prevalent in many scientific applications. In medicine, clinical events are often monitored between two clinical visits, makin…
Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes
Sophia Yazzourh, Nicolas Savy, Philippe Saint-Pierre +1
The goal of precision medicine is to provide individualized treatment at each stage of chronic diseases, a concept formalized by Dynamic Treatment Regimes (DTR). These regimes adap…
Penalized Q-Learning for Dynamic Treatment Regimes
Rui Song, Weiwei Wang, Donglin Zeng +1
A dynamic treatment regime effectively incorporates both accrued information and long-term effects of treatment from specially designed clinical trials. As these become more and mo…