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pro vyhledávání: '"McGrath Sean"'
Researchers would often like to leverage data from a collection of sources (e.g., primary studies in a meta-analysis) to estimate causal effects in a target population of interest. However, traditional meta-analytic methods do not produce causally in
Externí odkaz:
http://arxiv.org/abs/2402.04341
Autor:
McGrath, Sean, Zhao, XiaoFei, Ozturk, Omer, Katzenschlager, Stephan, Steele, Russell, Benedetti, Andrea
Publikováno v:
Res. Synth. Methods 15 (2024) 332-346
When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standard meta-analytic methods typically cannot be directly applied in this
Externí odkaz:
http://arxiv.org/abs/2302.14243
Autor:
McGrath, Sean, Mukherjee, Rajarshi
Estimators of doubly robust functionals typically rely on estimating two complex nuisance functions, such as the propensity score and conditional outcome mean for the average treatment effect functional. We consider the problem of how to estimate nui
Externí odkaz:
http://arxiv.org/abs/2212.14857
Autor:
McGrath, Sean, Katzenschlager, Stephan, Zimmer, Alexandra J., Seitel, Alexander, Steele, Russell, Benedetti, Andrea
Publikováno v:
Stat. Methods Med. Res. 32 (2023) 373-388
We consider the setting of an aggregate data meta-analysis of a continuous outcome of interest. When the distribution of the outcome is skewed, it is often the case that some primary studies report the sample mean and standard deviation of the outcom
Externí odkaz:
http://arxiv.org/abs/2206.14386
Autor:
Lui, Lizandro, Réquia, Weeberb J., dos Santos, Fernanda, Albert, Carlas Estefania, da Cruz Vieira, Luan, McGrath, Sean
Publikováno v:
In Vaccine 2 December 2024 42(26)
Publikováno v:
Epidemiology 33 (2022) 114-120
The parametric g-formula is an approach to estimating causal effects of sustained treatment strategies from observational data. An often cited limitation of the parametric g-formula is the g-null paradox: a phenomenon in which model misspecification
Externí odkaz:
http://arxiv.org/abs/2103.03857
Publikováno v:
Transactions on Machine Learning Research, 2023
As machine learning (ML) models are increasingly being employed to assist human decision makers, it becomes critical to provide these decision makers with relevant inputs which can help them decide if and how to incorporate model predictions into the
Externí odkaz:
http://arxiv.org/abs/2011.06167
Publikováno v:
In Science of the Total Environment 20 December 2023 905