Zobrazeno 1 - 10
of 123
pro vyhledávání: '"Liu, Molei"'
Despite recent advances in transfer learning with multiple source data sets, there still lacks developments for mixture target populations that could be approximated through a composite of the sources due to certain key factors like ethnicity in prac
Externí odkaz:
http://arxiv.org/abs/2407.20073
Autor:
Li, Siqi, Li, Xin, Yu, Kunyu, Miao, Di, Zhu, Mingcheng, Yan, Mengying, Ke, Yuhe, D'Agostino, Danny, Ning, Yilin, Wu, Qiming, Wang, Ziwen, Shang, Yuqing, Liu, Molei, Hong, Chuan, Liu, Nan
Clinical and biomedical research in low-resource settings often faces significant challenges due to the need for high-quality data with sufficient sample sizes to construct effective models. These constraints hinder robust model training and prompt r
Externí odkaz:
http://arxiv.org/abs/2407.11034
Conditional independence tests are crucial across various disciplines in determining the independence of an outcome variable $Y$ from a treatment variable $X$, conditioning on a set of confounders $Z$. The Conditional Randomization Test (CRT) offers
Externí odkaz:
http://arxiv.org/abs/2405.19231
Data fusion is an important way to realize powerful and generalizable analyses across multiple sources. However, different capability of data collection across the sources has become a prominent issue in practice. This could result in the blockwise m
Externí odkaz:
http://arxiv.org/abs/2405.18722
With the increasing availability of electronic health records (EHR) linked with biobank data for translational research, a critical step in realizing its potential is to accurately classify phenotypes for patients. Existing approaches to achieve this
Externí odkaz:
http://arxiv.org/abs/2404.01191
Surrogate variables in electronic health records (EHR) and biobank data play an important role in biomedical studies due to the scarcity or absence of chart-reviewed gold standard labels. We develop a novel approach named SASH for {\bf S}urrogate-{\b
Externí odkaz:
http://arxiv.org/abs/2302.04970
Autor:
Lyu, Liangbo, Liu, Molei
Doubly robust (DR) estimation is a crucial technique in causal inference and missing data problems. We propose a novel Propensity score Augmentved Doubly robust (PAD) estimator to enhance the commonly used DR estimator for average treatment effect on
Externí odkaz:
http://arxiv.org/abs/2301.02162
In this work, we propose a semi-supervised triply robust inductive transfer learning (STRIFLE) approach, which integrates heterogeneous data from label rich source population and label scarce target population to improve the learning accuracy in the
Externí odkaz:
http://arxiv.org/abs/2209.04977
Due to label scarcity and covariate shift happening frequently in real-world studies, transfer learning has become an essential technique to train models generalizable to some target populations using existing labeled source data. Most existing trans
Externí odkaz:
http://arxiv.org/abs/2208.05134
There have been increased concerns that the use of statins, one of the most commonly prescribed drugs for treating coronary artery disease, is potentially associated with the increased risk of new-onset type II diabetes (T2D). Nevertheless, to date,
Externí odkaz:
http://arxiv.org/abs/2205.06960