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pro vyhledávání: '"Huling, Jared"'
To determine the causal effect of a treatment using observational data, it is important to balance the covariate distributions between treated and control groups. However, achieving balance can be difficult when treated and control groups lack overla
Externí odkaz:
http://arxiv.org/abs/2410.12093
Recent research in causal inference has made important progress in addressing challenges to the external validity of trial findings. Such methods weight trial participant data to more closely resemble the distribution of effect-modifying covariates i
Externí odkaz:
http://arxiv.org/abs/2405.04419
Publikováno v:
Ann. Appl. Stat. 17 (4) 3384 - 3402, 2023
Individualized treatment rules (ITRs) for treatment recommendation is an important topic for precision medicine as not all beneficial treatments work well for all individuals. Interpretability is a desirable property of ITRs, as it helps practitioner
Externí odkaz:
http://arxiv.org/abs/2311.01538
While methods for measuring and correcting differential performance in risk prediction models have proliferated in recent years, most existing techniques can only be used to assess fairness across relatively large subgroups. The purpose of algorithmi
Externí odkaz:
http://arxiv.org/abs/2310.19988
Autor:
Jiang, Ziren, Huling, Jared D.
The effects of continuous treatments are often characterized through the average dose response function, which is challenging to estimate from observational data due to confounding and positivity violations. Modified treatment policies (MTPs) are an
Externí odkaz:
http://arxiv.org/abs/2310.11620
Randomized controlled trials (RCTs) are the gold standard for causal inference, but they are often powered only for average effects, making estimation of heterogeneous treatment effects (HTEs) challenging. Conversely, large-scale observational studie
Externí odkaz:
http://arxiv.org/abs/2306.17478
This work is motivated by the need to accurately model a vector of responses related to pediatric functional status using administrative health data from inpatient rehabilitation visits. The components of the responses have known and structured inter
Externí odkaz:
http://arxiv.org/abs/2302.11098
Autor:
Rott, Kollin W., Bronfort, Gert, Chu, Haitao, Huling, Jared D., Leininger, Brent, Murad, Mohammad Hassan, Wang, Zhen, Hodges, James S.
Meta-analysis is commonly used to combine results from multiple clinical trials, but traditional meta-analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to as
Externí odkaz:
http://arxiv.org/abs/2302.07840
Recent work has made important contributions in the development of causally-interpretable meta-analysis. These methods transport treatment effects estimated in a collection of randomized trials to a target population of interest. Ideally, estimates t
Externí odkaz:
http://arxiv.org/abs/2302.03544
Autor:
Huling, Jared D., Yu, Menggang
A key challenge in building effective regression models for large and diverse populations is accounting for patient heterogeneity. An example of such heterogeneity is in health system risk modeling efforts where different combinations of comorbiditie
Externí odkaz:
http://arxiv.org/abs/2212.12394