Two-phase designs for biomarker studies when disease processes are under intermittent observation [0.03%]
间歇性观察下生物标志物研究的两阶段设计
Kecheng Li,Richard J Cook
Kecheng Li
Multistate models offer an appealing framework for studying the onset and progression of chronic diseases in large cohort studies. Such studies often involve the collection and storage of biospecimens at an initial assessment, and intermitt...
Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events [0.03%]
结果事件存在下的多重结局的总体处理效应的非参数估计方法研究
Jessica Gronsbell,Zachary R McCaw,Isabelle-Emmanuella Nogues et al.
Jessica Gronsbell et al.
As standards of care advance, patients are living longer and once-fatal diseases are becoming manageable. Clinical trials increasingly focus on reducing disease burden, which can be quantified by the timing and occurrence of multiple non-fa...
Dongliang Zhang,Masoud Asgharian,Martin A Lindquist
Dongliang Zhang
Outlying observations are frequently encountered across a wide spectrum of scientific domains, posing notable challenges to the generalizability of statistical models and the reproducibility of downstream analysis. They are identified throu...
A regularized multi-state model for covariate selection with interval-censored survival data [0.03%]
一种用于区间删失生存数据的协变量选择的正则化多状态模型
Ariane Bercu,Agathe Guilloux,Cécile Proust-Lima et al.
Ariane Bercu et al.
In population-based cohorts, disease diagnoses are typically censored by intervals as made during scheduled follow-up visits. The exact disease onset time is thus unknown, and in the presence of semi-competing risk of death, subjects may al...
Discussion on "Nonparanormal adjusted marginal inference" by Susanne Dandl and Torsten Hothorn [0.03%]
苏珊娜·丹尔和托斯特恩·霍斯诺关于非参数调整的边注推理的讨论
Edward Bein
Edward Bein
In the context of randomized studies, a number of covariate-adjusted estimators for a variety of marginal treatment effects have been developed that are guaranteed to be consistent and asymptotically normal even when the statistical models ...
Knowledge-guided Bayesian biclustering model for omics data with noisy graphs [0.03%]
带有噪声图的omics数据的知识引导型贝叶斯双聚类模型
Qiyiwen Zhang,Wenrui Li,Suprateek Kundu et al.
Qiyiwen Zhang et al.
Extracting biologically meaningful information from high-dimensional, heterogeneous omics data is one of the key challenges in many biomedical studies. Among various biomedical applications, disease subtyping is of particular interest due t...
Causal inference targeting a concentration index for studies of health inequalities [0.03%]
因果推理针对集中指数以研究健康不平等现象
Mohammad Ghasempour,Xavier de Luna,Per E Gustafsson
Mohammad Ghasempour
A concentration index, a standardised covariance between a health variable and relative income ranks, is often used to quantify income-related health inequalities. There is a lack of formal approach to study the effect of an exposure, e.g.,...
Sijia Li,Rui Duan
Sijia Li
In response to the growing need for generating real-world evidence from multisite collaborative studies, we introduce an efficient collaborative learning approach to evaluate average treatment effect (ECO-ATE) in a multisite setting under d...
Likun Zhang,Wei Ma
Likun Zhang
Identifying treatment-covariate interaction is an initial step toward revealing treatment effect heterogeneity and advancing precision medicine. However, when the covariate of interest is continuous, the definition of treatment-covariate in...
Borrowing strength across exposures and outcomes via index models for multi-pollutant mixtures [0.03%]
基于指数模型的多污染物混合物暴露-效应联合解析方法研究
Glen McGee,Joseph Antonelli
Glen McGee
An important goal of environmental health research is to assess the health risks posed by mixtures of multiple environmental exposures. In these mixtures analyses, flexible models such as Bayesian kernel machine regression and multiple inde...