TITE-safety: a robust time-to-event safety monitoring approach for clinical trials [0.03%]
TITE-safety：临床试验中的一种稳健的时间事件安全性监测方法
Michael J Martens,Qinghua Lian,Brent R Logan
Michael J Martens
Safety evaluation is an essential component of clinical trials. To protect study participants, these studies often implement safety stopping rules that will halt the trial if an excessive number of toxicity events occur. Existing safety mon...
Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis [0.03%]
基于多重中间事件学习关联的生存动态预测及其在心血管病预后中的应用研究
Tonghui Yu,Liming Xiang
Tonghui Yu
Cardiovascular diseases are major causes of mortality globally. They often co-occur and are interrelated, leading to partial-order relationships among their onset times. However, these onset times are subject to informative censoring due to...
A Bayesian decision-theoretic approach to multiple testing in basket trials [0.03%]
Basket试验中多重假设检验的贝叶斯决策图方法研究
Amartya Kumar Maulik,Tianjian Zhou
Amartya Kumar Maulik
Basket trials evaluate a single treatment across multiple patient subpopulations, posing important multiple testing problems. Motivated by this setting, we propose a novel Bayesian decision-theoretic approach based on a family of loss funct...
Heterogeneous causal mediation analysis using Bayesian additive regression trees [0.03%]
基于Bayesian加性回归树的非同质因果中介分析
Chen Liu,Xu Qin,Victor B Talisa et al.
Chen Liu et al.
Causal mediation analysis provides insights into the mechanisms through which treatments affect outcomes. While mediation effects often vary across individuals, most existing methods focus solely on population-average effects, overlooking i...
Correcting random effect distributions to account for survivorship bias in individual heterogeneity Cormack-Jolly-Seber models [0.03%]
纠正随机效应分布以解决个体异质性CJS模型中的生存偏倚问题
Blanca Sarzo,Ruth King,Rachel McCrea
Blanca Sarzo
Survivorship (or selection) bias arises within statistical analyses where the observed data are subject to some underlying selection process prior to entry into the sampled data. For example, within capture-recapture studies, a primary sele...
Heterogeneity learning in distributed networks with large-scale survival data [0.03%]
基于大规模生存数据的分布式网络异质性学习方法研究
Tingting Cai,Tao Hu,Jianguo Sun et al.
Tingting Cai et al.
This paper considers survival analysis of large-scale data distributed across heterogeneous network nodes. We propose a novel method, the Distributed Spanning-Tree-Based Fused Lasso (DSTFL), for Cox regression in distributed settings. By em...
Mixed membership latent variable model with unknown factors, factor loadings and number of extreme profiles [0.03%]
未知因素、因子载荷和极端配置数量的混合成员隐变量模型
Yuyang He,Xinyuan Song,Kai Kang
Yuyang He
Mixed membership models are frequently utilized to capture complex individual heterogeneity in multivariate and longitudinal data. A key aspect of mixed membership modeling involves determining the number of extreme profiles (classes), a ta...
Joint modeling of multiple longitudinal biomarkers and survival outcomes via threshold regression: variability as a predictor [0.03%]
阈值回归联合建模多个纵向生物标志物和生存结局：以变异性作为预测因子
Mingyan Yu,Zhenke Wu,Michelle M Hood et al.
Mingyan Yu et al.
Longitudinal biomarker data and health outcomes are routinely collected in many studies to assess how biomarker trajectories predict health outcomes. Existing methods primarily focus on mean biomarker profiles, treating variability as a nui...
Q-Learning with clustered-SMART (cSMART) data: examining moderators in the construction of clustered adaptive interventions [0.03%]
基于聚类-SMART(cSMART)数据的Q学习：构建聚类自适应干预措施中的调节因素研究
Yao Song,Kelly Speth,Amy Kilbourne et al.
Yao Song et al.
A clustered adaptive intervention (cAI) is a prespecified sequence of decision rules that guides practitioners on how best-and based on which measures-to tailor cluster-level intervention to improve outcomes at the level of individuals with...
Uncertainty quantification and multi-stage variable selection for personalized treatment regimes [0.03%]
不确定量化和多阶段变量选择在个性化治疗方案中的应用
Jiefeng Bi,Matteo Borrotti,Bernardo Nipoti
Jiefeng Bi
A dynamic treatment regime is a sequence of medical decisions that adapts to the evolving clinical status of a patient over time. To facilitate personalized care, it is crucial to assess the probability of each available treatment option be...