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pro vyhledávání: '"Phillips, Rachael V."'
Background: Advanced methods for causal inference, such as targeted maximum likelihood estimation (TMLE), require certain conditions for statistical inference. However, in situations where there is not differentiability due to data sparsity or near-p
Externí odkaz:
http://arxiv.org/abs/2409.11265
Disruptions in clinical trials may be due to external events like pandemics, warfare, and natural disasters. Resulting complications may lead to unforeseen intercurrent events (events that occur after treatment initiation and affect the interpretatio
Externí odkaz:
http://arxiv.org/abs/2408.09060
Autor:
Alaa, Ahmed, Phillips, Rachael V., Kıcıman, Emre, Balzer, Laura B., van der Laan, Mark, Petersen, Maya
The validity of medical studies based on real-world clinical data, such as observational studies, depends on critical assumptions necessary for drawing causal conclusions about medical interventions. Many published studies are flawed because they vio
Externí odkaz:
http://arxiv.org/abs/2407.19118
The Targeted Maximum Likelihood Estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient and robust strategy for estimation and inference of
Externí odkaz:
http://arxiv.org/abs/2303.07329
Autor:
Malenica, Ivana, Phillips, Rachael V., Lazzareschi, Daniel, Coyle, Jeremy R., Pirracchio, Romain, van der Laan, Mark J.
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 associations important for the common model, while imposing a shared
Externí odkaz:
http://arxiv.org/abs/2301.12029
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 sources such as randomized controlled trials that make use of real-worl
Externí odkaz:
http://arxiv.org/abs/2208.07283
Autor:
Gruber, Susan, Phillips, Rachael V., Lee, Hana, Ho, Martin, Concato, John, van der Laan, Mark J.
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 indications for use for previously approved drugs, and to satisfy post-a
Externí odkaz:
http://arxiv.org/abs/2205.08643
Publikováno v:
International Journal of Epidemiology, Volume 52, Issue 4, August 2023, Pages 1276-1285
Common tasks encountered in epidemiology, including disease incidence estimation and causal inference, rely on predictive modeling. Constructing a predictive model can be thought of as learning a prediction function, i.e., a function that takes as in
Externí odkaz:
http://arxiv.org/abs/2204.06139
Autor:
Li, Haodong, Rosete, Sonali, Coyle, Jeremy, Phillips, Rachael V., Hejazi, Nima S., Malenica, Ivana, Arnold, Benjamin F., Benjamin-Chung, Jade, Mertens, Andrew, Colford Jr, John M., van der Laan, Mark J., Hubbard, Alan E.
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targe
Externí odkaz:
http://arxiv.org/abs/2109.14048
Autor:
Malenica, Ivana, Phillips, Rachael V., Pirracchio, Romain, Chambaz, Antoine, Hubbard, Alan, van der Laan, Mark J.
In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degrees of personalization. Namely, POSL optimizes predictions with respect
Externí odkaz:
http://arxiv.org/abs/2109.10452