Zobrazeno 1 - 10
of 985
pro vyhledávání: '"Gerds, Thomas A"'
Autor:
Munch, Anders, Gerds, Thomas A.
In survival analysis, prediction models are needed as stand-alone tools and in applications of causal inference to estimate nuisance parameters. The super learner is a machine learning algorithm which combines a library of prediction models into a me
Externí odkaz:
http://arxiv.org/abs/2405.17259
We consider estimation of conditional hazard functions and densities over the class of multivariate c\`adl\`ag functions with uniformly bounded sectional variation norm when data are either fully observed or subject to right-censoring. We demonstrate
Externí odkaz:
http://arxiv.org/abs/2404.11083
Autor:
Chen, David, Rytgaard, Helene C. W., Fong, Edwin C. H., Tarp, Jens M., Petersen, Maya L., van der Laan, Mark J., Gerds, Thomas A.
This article introduces the R package concrete, which implements a recently developed targeted maximum likelihood estimator (TMLE) for the cause-specific absolute risks of time-to-event outcomes measured in continuous time. Cross-validated Super Lear
Externí odkaz:
http://arxiv.org/abs/2310.19197
Autor:
Nance, Nerissa, Mertens, Andrew, Gerds, Thomas, Wang, Zeyi, Torp-Pedersen, Christian, van der Laan, Mark, Kvist, Kajsa, Lange, Theis, Zareini, Bochra, Petersen, Maya
The causal roadmap is a formal framework for causal and statistical inference that supports clear specification of the causal question, interpretable and transparent statement of required causal assumptions, robust inference, and optimal precision. T
Externí odkaz:
http://arxiv.org/abs/2310.03235
In situations with non-manipulable exposures, interventions can be targeted to shift the distribution of intermediate variables between exposure groups to define interventional disparity indirect effects. In this work, we present a theoretical study
Externí odkaz:
http://arxiv.org/abs/2305.10095
Autor:
Wang, Zeyi, van der Laan, Lars, Petersen, Maya, Gerds, Thomas, Kvist, Kajsa, van der Laan, Mark
Causal mediation analysis with random interventions has become an area of significant interest for understanding time-varying effects with longitudinal and survival outcomes. To tackle causal and statistical challenges due to the complex longitudinal
Externí odkaz:
http://arxiv.org/abs/2304.04904
Autor:
Sørensen, Kathrine Kold1 (AUTHOR) kathrine.kold.soerensen@regionh.dk, Gerds, Thomas Alexander2 (AUTHOR), Køber, Lars3,4 (AUTHOR), Loldrup Fosbøl, Emil3,4 (AUTHOR), Poulsen, Henrik Enghusen5 (AUTHOR), Møller, Amalie Lykkemark2,6,7 (AUTHOR), Andersen, Mikkel Porsborg1 (AUTHOR), Pedersen‐Bjergaard, Ulrik4,8 (AUTHOR), Torp‐Pedersen, Christian1,2 (AUTHOR), Zareini, Bochra1,2 (AUTHOR)
Publikováno v:
Journal of Diabetes. Oct2024, Vol. 16 Issue 10, p1-12. 12p.
This paper studies the generalization of the targeted minimum loss-based estimation (TMLE) framework to estimation of effects of time-varying interventions in settings where both interventions, covariates, and outcome can happen at subject-specific t
Externí odkaz:
http://arxiv.org/abs/2105.02088
In this paper we present a data-adaptive estimation procedure for estimation of average treatment effects in a time-to-event setting based on generalized random forests. In these kinds of settings, the definition of causal effect parameters are compl
Externí odkaz:
http://arxiv.org/abs/2104.13028