Propensity Score Modeling in Electronic Health Records with Time-to-Event Endpoints: Application to Kidney Transplantation [0.03%]
基于电子健康档案和事件发生时间终点的倾向性评分模型及其在肾脏移植中的应用研究
Jonathan W Yu,Dipankar Bandyopadhyay,Shu Yang et al.
Jonathan W Yu et al.
For large observational studies lacking a control group (unlike randomized controlled trials, RCT), propensity scores (PS) are often the method of choice to account for pre-treatment confounding in baseline characteristics, and thereby avoi...
Variable Selection with FDR Control for Noisy Data - An Application to Screening Metabolites that Are Associated with Breast Cancer and Colorectal Cancer [0.03%]
针对含噪声数据的变量选择及其在代谢组学研究中的应用：发现与乳腺癌和结直肠癌相关的生物标志物
Runqiu Wang,Ran Dai,Ying Huang et al.
Runqiu Wang et al.
The rapidly expanding field of metabolomics presents an invaluable resource for understanding the associations between metabolites and various diseases. However, the high dimensionality, presence of missing values, and measurement errors as...
Quantifying Direct and Indirect Effects through Joint Modeling of Terminal Events and Gap Times between Recurrent Events [0.03%]
通过终端事件和复发事件之间的时间间隔联合建模量化直接和间接效应
Fang Niu,Cheng Zheng,Lei Liu
Fang Niu
Joint models can describe the relationship between recurrent and terminal events. Typically, recurrent events are modeled using the total time scale, assuming constant covariate effects on each recurrent event. However, modeling the gap tim...
Neural Network for Correlated Survival Outcomes Using Frailty Model [0.03%]
基于脆弱性模型的生存分析相关结局神经网络方法研究
Ruiwen Zhou,Kevin He,Di Wang et al.
Ruiwen Zhou et al.
Extensive literature has been proposed for the analysis of correlated survival data. Subjects within a cluster share some common characteristics, e.g., genetic and environmental factors, so their time-to-event outcomes are correlated. The f...
Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior [0.03%]
基于混合高斯先验的大规模预训练变压器模型的幅度剪枝
Mingxuan Zhang,Yan Sun,Faming Liang
Mingxuan Zhang
Large pretrained transformer models have revolutionized modern AI applications with their state-of-the-art performance in natural language processing (NLP). However, their substantial parameter count poses challenges for real-world deployme...
EMixed: Probabilistic Multi-Omics Cellular Deconvolution of Bulk Omics Data [0.03%]
基于概率的多组学 bulk 数据细胞去卷积方法
Manqi Cai,Kangyi Zhao,Penghui Huang et al.
Manqi Cai et al.
Cellular deconvolution is a key approach to deciphering the complex cellular makeup of tissues by inferring the composition of cell types from bulk data. Traditionally, deconvolution methods have focused on a single molecular modality, rely...
A Meta-Learner Framework to Estimate Individualized Treatment Effects for Survival Outcomes [0.03%]
一种用于生存结局的个体治疗效应估计的元学习框架
Na Bo,Yue Wei,Lang Zeng et al.
Na Bo et al.
One crucial aspect of precision medicine is to allow physicians to recommend the most suitable treatment for their patients. This requires understanding the treatment heterogeneity from a patient-centric view, quantified by estimating the i...
An Innovative Method of Singular Spectrum Analysis to Conduct Gap-filling and Denoising on Time Series Data [0.03%]
一种新颖的 singular spectrum analysis 方法实现时间序列数据的填充和去噪
James J Yang,Anne Buu
James J Yang
Heart rate data collected from wearable devices - one type of time series data - could provide insights into activities, stress levels, and health. Yet, consecutive missing segments (i.e., gaps) that commonly occur due to improper device pl...
Scalable Community Extraction of Text Networks for Automated Grouping in Medical Databases [0.03%]
用于医疗数据库自动分组的文本网络可扩展社区提取方法
Tomilayo Komolafe,Allan Fong,Srijan Sengupta
Tomilayo Komolafe
Networks are ubiquitous in today's world. Community structure is a well-known feature of many empirical networks, and a lot of statistical methods have been developed for community detection. In this paper, we consider the problem of commun...
Central Posterior Envelopes for Bayesian Functional Principal Component Analysis [0.03%]
贝叶斯功能主成分分析的中央后包涵式
Joanna Boland,Donatello Telesca,Catherine Sugar et al.
Joanna Boland et al.
Bayesian methods provide direct inference in functional data analysis applications without reliance on bootstrap techniques. A major tool in functional data applications is the functional principal component analysis which decomposes the da...