134 citations · 148 across the 9 of their papers we have counts for
9 papers
[Invited Discussion] Randomization Tests to Address Disruptions in Clinical Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions
Rachael V. Phillips, Mark J. van der Laan
Disruptions in clinical trials may be due to external events like pandemics, warfare, and natural disasters. Resulting complications may lead to unforeseen intercurrent events (eve…
Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review
Matthew J. Smith, Rachael V. Phillips, Miguel Angel Luque-Fernandez +1
The Targeted Maximum Likelihood Estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least…
Multi-task Highly Adaptive Lasso
Ivana Malenica, Rachael V. Phillips, Daniel Lazzareschi +3
We propose a novel, fully nonparametric approach for the multi-task learning, the Multi-task Highly Adaptive Lasso (MT-HAL). MT-HAL simultaneously learns features, samples and task…
Evaluating and improving real-world evidence with Targeted Learning
Susan Gruber, Rachael V. Phillips, Hana Lee +2
Purpose: The Targeted Learning roadmap provides a systematic guide for generating and evaluating real-world evidence (RWE). From a regulatory perspective, RWE arises from diverse s…
Targeted learning: Towards a future informed by real-world evidence
Susan Gruber, Rachael V. Phillips, Hana Lee +3
The 21st Century Cures Act of 2016 includes a provision for the U.S. Food and Drug Administration (FDA) to evaluate the potential use of real-world evidence (RWE) to support new in…
Practical considerations for specifying a super learner
Rachael V. Phillips, Mark J. van der Laan, Hana Lee +1
Common tasks encountered in epidemiology, including disease incidence estimation and causal inference, rely on predictive modeling. Constructing a predictive model can be thought o…
Evaluating the Robustness of Targeted Maximum Likelihood Estimators via Realistic Simulations in Nutrition Intervention Trials
Haodong Li, Sonali Rosete, Jeremy Coyle +9
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, dou…
Personalized Online Machine Learning
Ivana Malenica, Rachael V. Phillips, Romain Pirracchio +3
In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degre…
Targeting Learning: Robust Statistics for Reproducible Research
Jeremy R. Coyle, Nima S. Hejazi, Ivana Malenica +9
Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions w…