A Bayesian approach for investigating the pharmacogenetics of combination antiretroviral therapy in people with HIV [0.03%]
使用贝叶斯方法探讨人类免疫缺陷病毒共用逆转录病毒疗法的药物遗传学
Wei Jin,Yang Ni,Amanda B Spence et al.
Wei Jin et al.
Combination antiretroviral therapy (ART) with at least three different drugs has become the standard of care for people with HIV (PWH) due to its exceptional effectiveness in viral suppression. However, many ART drugs have been reported to ...
Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator [0.03%]
使用残差生命价值估计器从电子医疗记录数据中估算最优治疗方案
Grace Rhodes,Marie Davidian,Wenbin Lu
Grace Rhodes
Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating ...
A Bayesian nonparametric approach for multiple mediators with applications in mental health studies [0.03%]
多个中介的贝叶斯非参数方法及其在精神健康研究中的应用
Samrat Roy,Michael J Daniels,Jason Roy
Samrat Roy
Mediation analysis with contemporaneously observed multiple mediators is a significant area of causal inference. Recent approaches for multiple mediators are often based on parametric models and thus may suffer from model misspecification. ...
Gopal Kotecha,Steffen Ventz,Sandra Fortini et al.
Gopal Kotecha et al.
The development and evaluation of novel treatment combinations is a key component of modern clinical research. The primary goals of factorial clinical trials of treatment combinations range from the estimation of intervention-specific effec...
DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders [0.03%]
DP2LM：利用深度学习方法处理高维中介和复杂混淆因素的中介效应估计与假设检验
Shuoyang Wang,Yuan Huang
Shuoyang Wang
Traditional linear mediation analysis has inherent limitations when it comes to handling high-dimensional mediators. Particularly, accurately estimating and rigorously inferring mediation effects is challenging, primarily due to the intertw...
Bayesian semiparametric model for sequential treatment decisions with informative timing [0.03%]
具有信息时间的序贯治疗决策的贝叶斯半参数模型
Arman Oganisian,Kelly D Getz,Todd A Alonzo et al.
Arman Oganisian et al.
We develop a Bayesian semiparametric model for the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in a phase III clinical...
Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data [0.03%]
协变量引导的样条专家混合贝叶斯模型在多变量高密度纵向数据分析中的应用分析
Haoyi Fu,Lu Tang,Ori Rosen et al.
Haoyi Fu et al.
With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usu...
Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes [0.03%]
全国观察性研究中可扩展的核平衡权重：医院盈利状态与心脏病发作结果的关系研究
Kwangho Kim,Bijan A Niknam,José R Zubizarreta
Kwangho Kim
Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the weights have minimal dispersion and thus produce a more stable ...
Andrew A Chen,Sarah M Weinstein,Azeez Adebimpe et al.
Andrew A Chen et al.
To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ subs...
A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes [0.03%]
一种贝叶斯多元因素分析模型：利用时间序列观测数据对混合结果进行因果推断
Pantelis Samartsidis,Shaun R Seaman,Abbie Harrison et al.
Pantelis Samartsidis et al.
Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model...