A new information integration framework for complex models with applications to real-world data [0.03%]
一种新的复杂模型信息整合框架及在实际问题中的应用
Jia Liang,Jason Falve,Shuo Chen et al.
Jia Liang et al.
Over the past few decades, various advanced methods have been developed to facilitate information integration. These methods leverage summary statistics (e.g. point estimates) from multiple sites or studies, which can be readily extracted f...
Chyong-Mei Chen,Yingwei Peng
Chyong-Mei Chen
Quantile regressions offer several attractive features, including the ability to allow covariate effects to vary at different quantile levels and to effectively handle heteroscedasticity in data, which makes it a viable alternative for anal...
Likelihood-based modeling of covariate-specific time-dependent receiver operating characteristic curves [0.03%]
基于可能性的协变量特定时变接收者操作特征曲线建模
Ainesh Sewak,Vanda Inácio,Joanne Wuu et al.
Ainesh Sewak et al.
Identifying reliable biomarkers for predicting clinical events in longitudinal studies is important for accurate disease prognosis and for guiding development of new treatments. However, prognostic studies are often observational, making it...
Estimating the effects of treatment regimes over the course of chronic disease: A multi-state causal framework with baseline confounding [0.03%]
基线混杂条件下的多状态因果框架：慢性病治疗效果的估计方法
Ming Ding
Ming Ding
The development of chronic disease is a long-term process involving multiple endpoints. Although multi-state Cox models can estimate state-specific survival risks over time, they are not well suited for comparing the effectiveness of treatm...
Handling missing data, skewness, and outliers in medical research: A robust factor analysis approach using the canonical fundamental skew- [Formula: see text] distribution [0.03%]
医疗研究中处理缺失数据、偏度和异常值的稳健因子分析方法——使用典范基本skew-[公式：见文本]-分布
Wan-Lun Wang,Luis M Castro,Tsung-I Lin
Wan-Lun Wang
Addressing incomplete and non-normally distributed multivariate data poses significant challenges in medical research, particularly when the interest is in discovering underlying data structures. This article introduces a robust factor anal...
Quantile inference for multivariate response regression in joint modeling of longitudinal and survival data [0.03%]
纵向数据和生存数据分析中多变量响应回归的分位数推断
Xiaoyu Niu,Xuejing Zhao
Xiaoyu Niu
Quantile regression (QR) offers a robust framework for analyzing covariate effects across the outcome distribution, particularly when the response variable exhibits skewness or heavy tails. To jointly model multivariate longitudinal biomark...
Empowering classification for multivariate functional data with simultaneous feature selection [0.03%]
兼具特征选择的多变量函数数据分类增强方法
Shuoyang Wang,Guanqun Cao,Yuan Huang
Shuoyang Wang
The opportunity to utilize multivariate functional data types for conducting classification tasks is emerging with the growing availability of imaging data. Inspired by the extensive data provided by the Alzheimer's Disease Neuroimaging Ini...
A Bayesian phase I/II platform design with survival efficacy endpoint for dose optimization [0.03%]
一种基于生存有效性的贝叶斯一期/二期平台设计的剂量优化方法
Xian Shi,Jiangyan Zhao,Jin Xu et al.
Xian Shi et al.
Motivated by a real-world drug development program, we propose a Bayesian phase I/II platform design to co-develop therapies with time-to-event efficacy endpoint (BPCT). We jointly model the binary toxicity outcome and the time-to-event eff...
Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs [0.03%]
基于神经网络模型和复杂抽样设计的美国糖尿病筛查研究
Marcos Matabuena,Juan C Vidal,Rahul Ghosal et al.
Marcos Matabuena et al.
Complex survey designs are widely used in medical cohort studies. Developing risk score models that adequately account for the sampling design is essential to minimize selection bias and obtain representative population estimates. This work...
PRO-ADD: Patient-empowered dose-finding trials integrating safety, preliminary efficacy and patient-reported outcomes for optimal dose selection [0.03%]
PRO-ADD：整合安全性和初步有效性的患者赋权剂量寻找试验，以期实现最优剂量选择
Emily Alger,Sumithra J Mandrekar,Jun Yin et al.
Emily Alger et al.
Advances in oncology drug development are driving the emergence of novel therapies, challenging traditional dose-efficacy assumptions in dose-finding oncology trials. Traditional trial designs aim to identify a maximum tolerated dose (MTD) ...