Suvra Pal,Suchitrita Sarkar Rathmann,Qi Jiang et al.
Suvra Pal et al.
Cancer remains the second most prevalent cause of death in the United States, claiming 605,213 lives in 2021, surpassing COVID-19 deaths. The cancer mortality rate continued to decline between 2019 and 2020, dropping by 1.5%, marking a sign...
Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models [0.03%]
基于跳跃维度的马尔可夫链蒙特卡罗方法和隐马尔可夫模型揭示癌症表观遗传改变
Farhad Shokoohi,Saeedeh Hajebi Khaniki
Farhad Shokoohi
Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this paper, we propose a ...
Doubly regularized generalized linear models for spatial observations with high-dimensional covariates [0.03%]
高维协变量空间观测的双重正则化广义线性模型
Arjun Sondhi,Si Cheng,Ali Shojaie
Arjun Sondhi
A discrete spatial lattice can be cast as a network structure over which spatially-correlated outcomes are observed. A second network structure may also capture similarities among measured features, when such information is available. Incor...
Adaptive Fisher's method using weakly geometric grid for combining p-values with application to COVID-19 surveillance [0.03%]
一种自适应的Fisher组合p值方法及其在新冠肺炎疫情监测中的应用
Yusi Fang,Zhao Ren,George C Tseng
Yusi Fang
In COVID-19 surveillance, detecting significant case increases within regions over specific periods is crucial. Classical methods, typically relying on strict parametric assumptions, struggle with the rare events characteristic of COVID-19'...
Robust domain selection for functional data via interval-wise testing and effect size mapping [0.03%]
基于区间检验和效应量映射的函数数据稳健领域选择
Yeonjoo Park,Aiguo Han
Yeonjoo Park
Among inferential problems in functional data analysis, domain selection is one of the practical interests aiming to identify sub-interval(s) of the domain where desired functional features are displayed. Motivated by applications in quanti...
Modelling spatial heterogeneity in exposure buffers and risk: a hierarchical Bayesian approach [0.03%]
模型化暴露缓冲区和风险中的空间异质性：层次贝叶斯方法
Saskia Comess,Daniel E Ho,Joshua L Warren
Saskia Comess
Place-based epidemiology studies often rely on circular buffers to define 'exposure' to spatially distributed risk factors, where the buffer radius represents a threshold beyond which exposure does not influence the outcome of interest. Thi...
Estimating the causal effects of multiple intermittent treatments with application to COVID-19 [0.03%]
用于估计间歇性治疗的因果效应以及在COVID-19方面的应用
Liangyuan Hu,Jiayi Ji,Himanshu Joshi et al.
Liangyuan Hu et al.
To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, we develop a joint marginal structural survival model and a novel weighting strategy to account for time-varying confou...
Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration [0.03%]
精准精神医学：通过数据整合预测抑郁症的异质性治疗效果
Carly L Brantner,Trang Quynh Nguyen,Harsh Parikh et al.
Carly L Brantner et al.
When treating depression, clinicians are interested in determining the optimal treatment for a given patient, which is challenging given the amount of treatments available. To advance individualized treatment allocation, integrating data ac...
Joint modelling of competing risks and current status data: an application to a spontaneous labour study [0.03%]
竞争风险和当前状态数据的联合建模：一项关于自发性分娩的研究的应用
Youjin Lee,Mei-Cheng Wang,Katherine L Grantz et al.
Youjin Lee et al.
The second stage of labour begins when the cervix is fully dilated and pushing begins until the fetus is delivered. A Caesarean delivery (CD) or operative vaginal delivery (OVD) is typically encouraged after the recommended time set by 'exp...
Robust sparse Bayesian regression for longitudinal gene-environment interactions [0.03%]
稳健的稀疏贝叶斯回归在纵向基因与环境相互作用中的应用
Kun Fan,Yu Jiang,Shuangge Ma et al.
Kun Fan et al.
In longitudinal studies, repeated measure analysis of variance (ANOVA) is a classical analysis where selecting important main and interaction effects for accurate estimation and prediction is among one of its central goals. With high-dimens...