Zobrazeno 1 - 10
of 985
pro vyhledávání: '"Van der Laan, Mark J"'
Autor:
Williamson, Brian D., Krakauer, Chloe, Johnson, Eric, Gruber, Susan, Shepherd, Bryan E., van der Laan, Mark J., Lumley, Thomas, Lee, Hana, Munoz, Jose J. Hernandez, Zhao, Fengyu, Dutcher, Sarah K., Desai, Rishi, Simon, Gregory E., Shortreed, Susan M., Nelson, Jennifer C., Shaw, Pamela A.
In pharmacoepidemiology, safety and effectiveness are frequently evaluated using readily available administrative and electronic health records data. In these settings, detailed confounder data are often not available in all data sources and therefor
Externí odkaz:
http://arxiv.org/abs/2412.15012
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
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
Constrained learning has become increasingly important, especially in the realm of algorithmic fairness and machine learning. In these settings, predictive models are developed specifically to satisfy pre-defined notions of fairness. Here, we study t
Externí odkaz:
http://arxiv.org/abs/2404.09847
Longitudinal settings involving outcome, competing risks and censoring events occurring and recurring in continuous time are common in medical research, but are often analyzed with methods that do not allow for taking post-baseline information into a
Externí odkaz:
http://arxiv.org/abs/2404.01736
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
Flexible estimation of the mean outcome under a treatment regimen (i.e., value function) is the key step toward personalized medicine. We define our target parameter as a conditional value function given a set of baseline covariates which we refer to
Externí odkaz:
http://arxiv.org/abs/2309.16099
Autor:
Hudson, Aaron, Geng, Elvin H., Odeny, Thomas A., Bukusi, Elizabeth A., Petersen, Maya L., van der Laan, Mark J.
The causal dose response curve is commonly selected as the statistical parameter of interest in studies where the goal is to understand the effect of a continuous exposure on an outcome.Most of the available methodology for statistical inference on t
Externí odkaz:
http://arxiv.org/abs/2306.07736
This study introduces a nonparametric definition of interaction and provides an approach to both interaction discovery and efficient estimation of this parameter. Using stochastic shift interventions and ensemble machine learning, our approach identi
Externí odkaz:
http://arxiv.org/abs/2305.01849
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