Nonparametric ANCOVA for longitudinal outcomes in a randomized clinical trial [0.03%]
随机对照试验中纵向结局的非参数协方差分析
Rex Shen,Xiaotong Jiang,Changyu Shen et al.
Rex Shen et al.
The analysis of covariance (ANCOVA) is a commonly used method for correcting bias and improving accuracy in estimating the average treatment effect in randomized clinical trials. In this paper, we focus on using ANCOVA for longitudinal outc...
A robust covariate-balancing method for estimating individualized treatment with censored data [0.03%]
一种稳健的协变量平衡方法，用于估计删失数据下的个体化治疗规则
Rujia Zheng,Wensheng Zhu,Xiaofan Guo
Rujia Zheng
One of the most essential aspects of precision medicine is the identification of optimal individualized treatment regimen, which recommends treatment decisions to maximize a patient's expected survival time based on their individual charact...
SIMBA-a Bayesian decision framework for the identification of optimal biomarker subgroups for cancer basket clinical trials [0.03%]
基于癌症篮子临床试验的最优生物标志物亚组识别的贝叶斯决策框架SIMBA
Shijie Yuan,Jiaxin Liu,Zhihua Gong et al.
Shijie Yuan et al.
Motivated by a multi-indication basket trial aiming to assess the efficacy of a novel biomarker-targeted therapy in gastric or gastroesophageal junction (G/GEJ), pancreatic, and other related cancers, we consider a statistical design and de...
Yonghyun Kwon,Jae Kwang Kim,Yumou Qiu
Yonghyun Kwon
Statistical analysis of voluntary survey data is an important area of research in survey sampling. We consider a unified approach to voluntary survey data analysis under the assumption that the sampling mechanism is ignorable. Generalized e...
Yuanzhen Yue,Stella Self,Yichao Wu et al.
Yuanzhen Yue et al.
Modern biomedical studies frequently collect complex, high-dimensional physiological signals using wearables and sensors along with time-to-event outcomes, making efficient variable selection methods crucial for interpretation and improving...
DNN-based semiparametric AFT model for integrating genomic and pathological imaging data in cancer prognosis [0.03%]
基于DNN的半参数AFT模型在癌症预后中的应用：基因组和病理影像数据的整合
Jingmao Li,Qingzhao Zhang,Shuangge Ma
Jingmao Li
Modeling prognosis has critical implications in cancer research and clinical practice. Many studies have been conducted, built on genomic (and omics in general) and pathological imaging data. In recent research, a handful of studies have al...
Inference for microbe-metabolite association networks using a latent graph model [0.03%]
基于潜在图模型的微生态系统中微生物与代谢物关联推断方法研究
Jing Ma
Jing Ma
Correlation networks are commonly used to infer associations between microbes and metabolites. The resulting $p$-values are then corrected for multiple comparisons using existing methods such as the Benjamini & Hochberg (BH) procedure to co...
Comment on "Double robust conditional independence test for novel biomarkers given established risk factors with survival data" [0.03%]
关于“生存数据下同时考虑已知风险因素的新型生物标志物稳健性条件独立性检验”的讨论
Lucas Kook
Lucas Kook
In their paper, Yang et al. tackle the important challenge of identifying biomarkers that are predictive for a time-to-event response while taking into account relevant risk factors. The proposed solution is a doubly robust conditional inde...
Reduced varying coefficient models for regional quantile regression with multiple responses [0.03%]
具有多重响应的区域分位数回归的简约可变系数模型
Woorim Jung,Seyoung Park,Hyokyoung G Hong et al.
Woorim Jung et al.
Analyzing multiple outcome variables via regional quantile regression in high-dimensional settings poses significant statistical and computational challenges. In this paper, we propose a new framework that models multivariate quantile varyi...
OPERA: a new algorithm for patient stratification based on partially ordered risk factors [0.03%]
基于部分有序风险因素的新型患者分层算法Opera研究
Yingzhou Liu,Menggang Yu
Yingzhou Liu
Risk stratification is an invaluable tool for modern healthcare systems. By separating patients into subgroups with distinct disease severity and prognosis, it allows better clinical decision making due to targeted care thus ultimately fost...