SPLasso for high-dimensional additive hazards regression with covariate measurement error [0.03%]
具有高维加性风险回归和协变量测量误差的SPLasso
Jiarui Zhang,Hongsheng Liu,Xin Chen et al.
Jiarui Zhang et al.
High-dimensional error-prone survival data are prevalent in biomedical studies, where numerous clinical or genetic variables are collected for risk assessment. The presence of measurement errors in covariates complicates parameter estimatio...
Statistical inference for heterogeneous treatment effect with right-censored data from synthesizing randomized clinical trials and real-world data [0.03%]
合成随机临床试验和真实世界数据下的右删失数据异质性治疗效应的统计推断
Guangcai Mao,Shu Yang,Xiaofei Wang
Guangcai Mao
The heterogeneous treatment effect plays a crucial role in precision medicine. There is evidence that real-world data, even subject to biases, can be employed as supplementary evidence for randomized clinical trials to improve the statistic...
Inverse-intensity weighted generalized estimating equations for longitudinal data subject to irregular observation: which variables should be included in the visit rate model? [0.03%]
基于不规则观察的纵向数据的逆强度加权广义估计方程：哪些变量应该包含在访问率模型中？
Eleanor M Pullenayegum,Di Shan
Eleanor M Pullenayegum
Longitudinal data are often subject to irregular and informative visit times. Weighting generalized estimating equations by the inverse of the visit rate yields asymptotically unbiased estimates of regression coefficients provided that outc...
A meta-learning method for estimation of causal excursion effects to assess time-varying moderation [0.03%]
一种用于估计因果范围效应以评估时间变化的调节作用的元学习方法
Jieru Shi,Walter Dempsey
Jieru Shi
Advances in wearable technologies and health interventions delivered by smartphones have greatly increased the accessibility of mobile health (mHealth) interventions. Micro-randomized trials (MRTs) are designed to assess the effectiveness o...
Shuqi Wang,Ying Yuan,Suyu Liu
Shuqi Wang
The US Food and Drug Administration (FDA) launched Project Optimus to shift the objective of dose selection from the maximum tolerated dose to the optimal biological dose (OBD), optimizing the benefit-risk tradeoff. One approach recommended...
Spatially aware adjusted Rand index for evaluating spatial transcriptomics clustering [0.03%]
用于评估空间转录组学聚类的空间感知调整兰德指数
Yinqiao Yan,Xiangnan Feng,Xiangyu Luo
Yinqiao Yan
The spatial transcriptomics (ST) clustering plays a crucial role in elucidating the tissue spatial heterogeneity. An accurate ST clustering result can greatly benefit downstream biological analyses. As various ST clustering approaches are p...
A Bayesian semiparametric mixture model for clustering zero-inflated microbiome data [0.03%]
一种用于零膨胀微生物组数据聚类的贝叶斯半参数混合模型
Suppapat Korsurat,Matthew D Koslovsky
Suppapat Korsurat
Microbiome research has immense potential for unlocking insights into human health and disease. A common goal in human microbiome research is identifying subgroups of individuals with similar microbial composition that may be linked to spec...
The Cox-Pólya-Gamma algorithm for flexible Bayesian inference of multilevel survival models [0.03%]
Cox-Pólya-Gamma算法在多水平生存模型的灵活贝叶斯推理中的应用
Benny Ren,Jeffrey S Morris,Ian Barnett
Benny Ren
Bayesian Cox semiparametric regression is an important problem in many clinical settings. The elliptical information geometry of Cox models is underutilized in Bayesian inference but can effectively bridge survival analysis and hierarchical...
Yinan Lin,Zhenhua Lin
Yinan Lin
Inspired by logistic regression, we introduce a regression model for data tuples consisting of a binary response and a set of covariates residing in a metric space without vector structures. Based on the proposed model, we also develop a bi...