Histopathological imaging-based cancer heterogeneity analysis via penalized fusion with model averaging [0.03%]
基于惩罚融合和模型平均的组织病理图像癌症异质性分析
Baihua He,Tingyan Zhong,Jian Huang et al.
Baihua He et al.
Heterogeneity is a hallmark of cancer. For various cancer outcomes/phenotypes, supervised heterogeneity analysis has been conducted, leading to a deeper understanding of disease biology and customized clinical decisions. In the literature, ...
Bayesian variable selection for non-Gaussian responses: a marginally calibrated copula approach [0.03%]
非高斯响应的贝叶斯变量选择：一种边缘校准的copula方法
Nadja Klein,Michael Stanley Smith
Nadja Klein
We propose a new highly flexible and tractable Bayesian approach to undertake variable selection in non-Gaussian regression models. It uses a copula decomposition for the joint distribution of observations on the dependent variable. This al...
Xing Gao,Sungwon Lee,Gen Li et al.
Xing Gao et al.
Multiblock data, where multiple groups of variables from different sources are observed for a common set of subjects, are routinely collected in many areas of science. Methods for joint factorization of such multiblock data are being develo...
Regularized matrix data clustering and its application to image analysis [0.03%]
正则化矩阵数据聚类及其在图像分析中的应用
Xu Gao,Weining Shen,Liwen Zhang et al.
Xu Gao et al.
We propose a novel regularized mixture model for clustering matrix-valued data. The proposed method assumes a separable covariance structure for each cluster and imposes a sparsity structure (eg, low rankness, spatial sparsity) for the mean...
Nonparametric trend estimation in functional time series with application to annual mortality rates [0.03%]
功能性时间序列中非参数趋势估计及在年度死亡率中的应用
Israel Martínez-Hernández,Marc G Genton
Israel Martínez-Hernández
We address the problem of trend estimation for functional time series. Existing contributions either deal with detecting a functional trend or assuming a simple model. They consider neither the estimation of a general functional trend nor t...
Yi Zhao,Lexin Li,Brian S Caffo
Yi Zhao
With advancements in technology, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Some notable examples include multimodal neuroimaging studies for the simultaneous investigation o...
Brittany Green,Heng Lian,Yan Yu et al.
Brittany Green et al.
As ultra high-dimensional longitudinal data are becoming ever more apparent in fields such as public health and bioinformatics, developing flexible methods with a sparse model is of high interest. In this setting, the dimension of the covar...
Qingzhi Zhong,Huazhen Lin,Yi Li
Qingzhi Zhong
Gaussian distributions have been commonly assumed when clustering functional data. When the normality condition fails, biased results will follow. Additional challenges occur as the number of the clusters is often unknown a priori. This pap...
A Bayesian adaptive phase I/II clinical trial design with late-onset competing risk outcomes [0.03%]
具有晚发竞争风险结果的I/II 期贝叶斯自适应临床试验设计
Yifei Zhang,Sha Cao,Chi Zhang et al.
Yifei Zhang et al.
Early-phase dose-finding clinical trials are often subject to the issue of late-onset outcomes. In phase I/II clinical trials, the issue becomes more intractable because toxicity and efficacy can be competing risk outcomes such that the occ...
Clinical Trial
Biometrics. 2021 Sep;77(3):796-808. DOI:10.1111/biom.13347 2021
Chuan Hong,Georgia Salanti,Sally C Morton et al.
Chuan Hong et al.
Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analyses. The most well-known reasons for small study effects inc...
Meta-Analysis
Biometrics. 2020 Dec;76(4):1240-1250. DOI:10.1111/biom.13342 2020