Risk estimation and dynamic prediction using discrete-time joint models for longitudinal and multistate data with interval and state censoring [0.03%]
基于区间和状态删失的纵向与多状态数据离散时间联合模型的风险评估及动态预测方法研究
Lu You,Falastin Salami,Carina Törn et al.
Lu You et al.
This paper presents a joint model of multivariate longitudinal data and multistate data with application to modeling and predicting autoantibody development in The Environmental Determinants of Diabetes in the Young (TEDDY) study. The model...
A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome [0.03%]
一种贝叶斯函数并发零膨胀狄利克雷多元模型及其在婴儿微生物组中的应用
Brody Erlandson,Ander Wilson,Matthew D Koslovsky
Brody Erlandson
The infant microbiome undergoes rapid changes in composition over time and is associated with long-term risks of conditions such as immune strength, allergy, asthma, and other health outcomes. Modeling the associations between exposures or ...
Towards optimal environmental policies: policy learning under arbitrary bipartite network interference [0.03%]
迈向最佳环境政策：任意二部图干扰下的政策学习
Raphael C Kim,Falco J Bargagli-Stoffi,Kevin L Chen et al.
Raphael C Kim et al.
The substantial effect of air pollution on cardiovascular disease and mortality burdens is well-established. Emissions-reducing interventions on coal-fired power plants-a major source of hazardous air pollution-have proven to be an effectiv...
Álvaro Méndez-Civieta,Ying Wei,Jeff Goldsmith
Álvaro Méndez-Civieta
Accelerometer studies typically include repeated 24-h observations over several days and more recently have collected information on participants for weeks, months, or years. Meanwhile, there is growing evidence to suggest that components o...
Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment [0.03%]
基于疾病风险评估的生存分析自适应迁移学习方法研究
Yuying Lu,Tian Gu,Rui Duan
Yuying Lu
To address the challenges in modeling time-to-event outcomes in small-sample settings, we propose a novel transfer learning approach, termed CoxTL, based on the widely used Cox proportional hazards model, accounting for potential covariate ...
Rongrong Wang,Shrabanti Chowdhury,Hanwen Huang et al.
Rongrong Wang et al.
Advances in data acquisition technologies and computational resources have significantly improved the analysis of large datasets across various domains. These datasets often feature a high number of variables but a limited number of observa...
NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis [0.03%]
基于负二项分布的微阵列数据分类模型
Seong-Hwan Jun,Marc K Halushka,Matthew N McCall
Seong-Hwan Jun
MicroRNAs play a central role in the regulation of gene expression and the modulation of diseases. Despite their importance, statistical methods for analyzing microRNAs have received less attention compared to messenger RNAs. Critically, me...
Addressing the influence of unmeasured confounding in observational studies with time-to-event outcomes: a semiparametric sensitivity analysis approach [0.03%]
具有时间至事件结果的观察性研究中未测量混杂因素影响的半参数敏感性分析方法
Linda Amoafo,Shiyao Xu,Elizabeth Platz et al.
Linda Amoafo et al.
In this paper, we develop a semiparametric sensitivity analysis approach designed to address unmeasured confounding in observational studies with time-to-event outcomes. We target estimation of the marginal distributions of potential outcom...
IV-learner: learning conditional average treatment effects using instrumental variables [0.03%]
使用工具变量学习条件平均处理效应
Stijn Vansteelandt,Stephen O&#x;Neill,Richard Grieve et al.
Stijn Vansteelandt et al.
A live clinical question is: which patients benefit from intensive care unit (ICU) transfer? Motivated by this question we address the problem of estimating conditional average treatment effects (CATE) in the presence of unmeasured confound...
Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data [0.03%]
单细胞RNA测序数据中的广义矩阵分解模型的随机梯度下降估计方法
Cristian Castiglione,Alexandre Segers,Lieven Clement et al.
Cristian Castiglione et al.
Single-cell RNA sequencing allows the quantification of gene expression at the individual cell level, enabling the study of cellular heterogeneity and gene expression dynamics. Dimensionality reduction is a common preprocessing step critica...