Signpost Testing to Navigate the Parameter Space of the Gaussian Graphical Model With High-Dimensional Data [0.03%]
高维数据下单变量选择在具方差组的图模型参数空间导航中的作用
Kai Ruan,Mark A van de Wiel,Wessel N van Wieringen
Kai Ruan
We evaluate the relevance of external quantitative information on the parameter of a Gaussian graphical model from high-dimensional data. This information comes in the form of a parameter value available from a related knowledge domain or p...
Time-Dependent Mediators in Survival Analysis: Graphical Representation of Causal Assumptions [0.03%]
生存分析中的时变中介因素：因果假设的图形表示方法
Søren Wengel Mogensen,Odd O Aalen,Susanne Strohmaier
Søren Wengel Mogensen
We study time-dependent mediators in survival analysis using a treatment separation approach due to Didelez [Lifetime Data Analysis 25, no. 4: 593-610] and based on earlier work by Robins and Richardson [Causality and Psychopathology: Findi...
The Challenge of Time-to-Event Analysis for Multiple Events: A Guided Tour From Time-to-First-Event to Recurrent Time-to-Event Analysis [0.03%]
多重事件时间事件分析的挑战：从初次事件时间到反复事件时间分析的指南式探讨
Sandra Schmeller,Alexandra Erdmann,Jan Beyersmann et al.
Sandra Schmeller et al.
Clinical trials often compare a treatment to a control group concerning multiple possible combined time-to-event endpoints like hospital-free survival. Thereby, the first endpoint may occur more than once ("recurrent"), whereas the second e...
Analysis of Multiple Outcomes in Contaminated Trials Reinforced With Validation Data [0.03%]
补充验证数据的污染试验的多重结果分析方法研究
Solomon W Harrar,Zi Ye
Solomon W Harrar
This paper is concerned with estimation and testing for treatment effects with multivariate outcomes. It primarily focuses on the situation where imperfect diagnostic tools are used to classify subjects into different groups. Oftentimes, th...
Time-Dependent Predictive Accuracy Metrics in the Context of Interval Censoring and Competing Risks [0.03%]
区间截断和竞争风险下的时间依赖预测准确性指标
Zhenwei Yang,Dimitris Rizopoulos,Lisa F Newcomb et al.
Zhenwei Yang et al.
Evaluating the performance of a prediction model is a common task in medical statistics. Standard accuracy metrics require the observation of the true outcomes. This is typically not possible in the setting with time-to-event outcomes due t...
Informative Co-Data Learning for High-Dimensional Horseshoe Regression [0.03%]
用于高维数据岭回归的 informative co-data 学习方法研究
Claudio Busatto,Mark A van de Wiel
Claudio Busatto
High-dimensional data often arise from clinical genomics research to infer relevant predictors of a particular trait. A way to improve the predictive performance is by incorporating information about the predictors obtained from existing fr...
Modified Skew Discrete Laplace Regression Models for Integer-Valued Data With Applications to Paired Samples [0.03%]
用于整数值数据的修正斜率离散拉普拉斯回归模型及其在配对样本中的应用
Rodrigo M R de Medeiros,Marcelo Bourguignon
Rodrigo M R de Medeiros
Modeling events associated with discrete-valued observations arises in several practical situations. Until now, research on statistical methods for discrete data has primarily focused on modeling count data. Nevertheless, discrete observati...
A Covariance-Based Penalty Estimator for Model Assessment With Censored Data [0.03%]
基于协方差的罚分估计量用于删失数据模型评估
Zhuoran Zhang,Daniel L Gillen
Zhuoran Zhang
Prediction model selection and assessment are primary objectives of many statistical analyses. Covariance-based penalty estimators provide analytic estimates of the optimism associated with naive training error estimates for multiple classe...
Paul Blanche,Frank Eriksson
Paul Blanche
In the competing risks setting, the t $t$ -year absolute risk for a specific time t $t$ (e.g., 2 years), also called the cumulative incidence function at time t $t$ , is often interesting to estimate. It is routinely estimated using the ...
Comparative Study
Biometrical journal. Biometrische Zeitschrift. 2025 Dec;67(6):e70104. DOI:10.1002/bimj.70104 2025
Dimension Reduction for the Conditional Quantiles of Functional Data With Categorical Predictors [0.03%]
含类别预测变量的功能数据条件分位数降维
Shanshan Wang,Eliana Christou,Eftychia Solea et al.
Shanshan Wang et al.
Functional data analysis has received significant attention due to its frequent occurrence in modern applications, such as in the medical field, where electrocardiograms or electroencephalograms can be used for a better understanding of var...