Evaluating dynamic and predictive discrimination for recurrent event models: use of a time-dependent C-index [0.03%]
评估递归事件模型的动态和预测辨别能力：使用时间相关的C指数
Jian Wang,Xinyang Jiang,Jing Ning
Jian Wang
Interest in analyzing recurrent event data has increased over the past few decades. One essential aspect of a risk prediction model for recurrent event data is to accurately distinguish individuals with different risks of developing a recur...
Sarah Teichman,Michael D Lee,Amy D Willis
Sarah Teichman
Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to differe...
Signal detection statistics of adverse drug events in hierarchical structure for matched case-control data [0.03%]
分层匹配病例对照数据中的不良事件的信号检测统计量研究
Seok-Jae Heo,Sohee Jeong,Dagyeom Jung et al.
Seok-Jae Heo et al.
The tree-based scan statistic is a data mining method used to identify signals of adverse drug reactions in a database of spontaneous reporting systems. It is particularly beneficial when dealing with hierarchical data structures. One may u...
Peter B Gilbert,Youyi Fong,Avi Kenny et al.
Peter B Gilbert et al.
An immune correlate of risk (CoR) is an immunologic biomarker in vaccine recipients associated with an infectious disease clinical endpoint. An immune correlate of protection (CoP) is a CoR that can be used to reliably predict vaccine effic...
A Bayesian nonparametric approach to correct for underreporting in count data [0.03%]
纠正计数数据欠报的贝叶斯非参数方法
Serena Arima,Silvia Polettini,Giuseppe Pasculli et al.
Serena Arima et al.
We propose a nonparametric compound Poisson model for underreported count data that introduces a latent clustering structure for the reporting probabilities. The latter are estimated with the model's parameters based on experts' opinion and...
Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research [0.03%]
具有回顾性时间尺度上信息删失的联合模型在姑息护理研究中的应用
Quran Wu,Michael Daniels,Areej El-Jawahri et al.
Quran Wu et al.
Joint modeling of longitudinal data such as quality of life data and survival data is important for palliative care researchers to draw efficient inferences because it can account for the associations between those two types of data. Modeli...
Improved fMRI-based pain prediction using Bayesian group-wise functional registration [0.03%]
基于改进的贝叶斯组功能配准的疼痛fMRI预测方法研究
Guoqing Wang,Abhirup Datta,Martin A Lindquist
Guoqing Wang
In recent years, the field of neuroimaging has undergone a paradigm shift, moving away from the traditional brain mapping approach towards the development of integrated, multivariate brain models that can predict categories of mental events...
Semi-supervised mixture multi-source exchangeability model for leveraging real-world data in clinical trials [0.03%]
一种半监督混合多源交换性模型，用于在临床试验中利用真实世界数据
Lillian M F Haine,Thomas A Murry,Raquel Nahra et al.
Lillian M F Haine et al.
The traditional trial paradigm is often criticized as being slow, inefficient, and costly. Statistical approaches that leverage external trial data have emerged to make trials more efficient by augmenting the sample size. However, these app...
Nathan W Bean,Joseph G Ibrahim,Matthew A Psioda
Nathan W Bean
In recent years, multi-regional clinical trials (MRCTs) have increased in popularity in the pharmaceutical industry due to their ability to accelerate the global drug development process. To address potential challenges with MRCTs, the Inte...
Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms [0.03%]
高维离散结果的个性化治疗方案中的变量选择：降低抑郁症状的严重性
Erica E M Moodie,Zeyu Bian,Janie Coulombe et al.
Erica E M Moodie et al.
Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use...