Zobrazeno 1 - 10
of 953
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
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
Publikováno v:
Biometrical Journal 2020
We are interested in the estimation of average treatment effects based on right-censored data of an observational study. We focus on causal inference of differences between t-year absolute event risks in a situation with competing risks. We derive do
Externí odkaz:
http://arxiv.org/abs/1907.12912