Zobrazeno 1 - 10
of 1 363
pro vyhledávání: '"van Der Laan, Mark"'
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
This manuscript explores the intersection of surrogate outcomes and adaptive designs in statistical research. While surrogate outcomes have long been studied for their potential to substitute long-term primary outcomes, current surrogate evaluation m
Externí odkaz:
http://arxiv.org/abs/2408.02667
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
In environmental epidemiology, identifying subpopulations vulnerable to chemical exposures and those who may benefit differently from exposure-reducing policies is essential. For instance, sex-specific vulnerabilities, age, and pregnancy are critical
Externí odkaz:
http://arxiv.org/abs/2406.10792
We consider the problem of estimating the average treatment effect (ATE) when both randomized control trial (RCT) data and real-world data (RWD) are available. We decompose the ATE estimand as the difference between a pooled-ATE estimand that integra
Externí odkaz:
http://arxiv.org/abs/2405.07186
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
Autor:
Shirakawa, Toru, Li, Yi, Wu, Yulun, Qiu, Sky, Li, Yuxuan, Zhao, Mingduo, Iso, Hiroyasu, van der Laan, Mark
We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies in longitudinal problem settings. Our approach utilizes a transformer a
Externí odkaz:
http://arxiv.org/abs/2404.04399
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
We consider estimation of a functional parameter of a realistically modeled data distribution based on independent and identically distributed observations. Suppose that the true function is defined as the minimizer of the expectation of a specified
Externí odkaz:
http://arxiv.org/abs/2312.16953