Network Meta-Analysis of Time-to-Event Endpoints With Individual Participant Data Using Restricted Mean Survival Time Regression [0.03%]
基于个体患者数据的生存终点网络荟萃分析采用受限平均生存时间回归方法
Kaiyuan Hua,Xiaofei Wang,Hwanhee Hong
Kaiyuan Hua
Network meta-analysis (NMA) extends pairwise meta-analysis to compare multiple treatments simultaneously by combining "direct" and "indirect" comparisons of treatments. The availability of individual participant data (IPD) makes it possible...
Kaeum Choi,Jeong Hoon Jang
Kaeum Choi
A decision to adopt a new medical device requires a rigorous assessment of the reliability and reproducibility of its clinical measurements. In this paper, with the goal of establishing the validity and acceptability of modern high-tech med...
High-Dimensional Variable Selection With Competing Events Using Cooperative Penalized Regression [0.03%]
使用合作惩罚回归进行高维变量选择以应对竞争事件
Lukas Burk,Andreas Bender,Marvin N Wright
Lukas Burk
Variable selection is an important step in the analysis of high-dimensional data, yet there are limited options for survival outcomes in the presence of competing risks. Commonly employed penalized Cox regression considers each event type s...
Parametric Estimation of the Mean Number of Events in the Presence of Competing Risks [0.03%]
具有竞争风险情况下平均事件数量的参数估计
Joshua P Entrop,Lasse H Jakobsen,Michael J Crowther et al.
Joshua P Entrop et al.
Recurrent events, for example, hospitalizations or drug prescriptions, are common in time-to-event research. One useful summary measure of the recurrent event process is the mean number of events. Methods for estimating the mean number of e...
Mediation Analysis With Exposure-Mediator Interaction and Covariate Measurement Error Under the Additive Hazards Model [0.03%]
具有暴露-中介相互作用和协变量测量误差的介导分析在加性风险模型下的应用研究
Ying Yan,Lingzhu Shen
Ying Yan
Causal mediation analysis is a useful tool to examine how an exposure variable causally affects an outcome variable through an intermediate variable. In recent years, there is increasing research interest in mediation analysis with survival...
A Bias-Corrected Bayesian Nonparametric Model for Combining Studies With Varying Quality in Meta-Analysis [0.03%]
一种校正了偏差的贝叶斯非参数模型：用于元分析中质量各异的研究间的整合问题
Pablo Emilio Verde,Gary L Rosner
Pablo Emilio Verde
Bayesian nonparametric (BNP) approaches for meta-analysis have been developed to relax distributional assumptions and handle the heterogeneity of random effects distributions. These models account for possible clustering and multimodality o...
Mario Hasler,Tim Birr,Ludwig A Hothorn
Mario Hasler
This paper proposes a general approach for handling multiple contrast tests for normally distributed data in the presence of partial heteroskedasticity. In contrast to the usual case of complete heteroskedasticity, the treatments belong to ...
Quantification of Difference in Nonselectivity Between In Vitro Diagnostic Medical Devices [0.03%]
体外诊断医疗器械非特异性差异的量化分析
Pernille Kjeilen Fauskanger,Sverre Sandberg,Jesper Johansen et al.
Pernille Kjeilen Fauskanger et al.
Correct measurement results from in vitro diagnostic (IVD) medical devices (MD) are crucial for optimal patient care. The performance of IVD-MDs is often assessed through method comparison studies. Such studies can be compromised by the inf...
Developing and Comparing Four Families of Bayesian Network Autocorrelation Models for Binary Outcomes: Estimating Peer Effects Involving Adoption of Medical Technologies [0.03%]
四种贝叶斯网络自相关模型族的开发和比较：估计涉及采用医疗技术的同伴效应
Guanqing Chen,A James O&#x;Malley
Guanqing Chen
Despite the extensive use of network autocorrelation models in social network analysis, network autocorrelation models for binary dependent variables have received surprisingly scant attention. In this paper, we develop four network autocor...
Comparative Study
Biometrical journal. Biometrische Zeitschrift. 2025 Feb;67(1):e70030. DOI:10.1002/bimj.70030 2025
Sensitivity Analysis for Effects of Multiple Exposures in the Presence of Unmeasured Confounding [0.03%]
多重暴露影响的敏感性分析在未测量混杂因素存在的情况下
Boram Jeong,Seungjae Lee,Shinhee Ye et al.
Boram Jeong et al.
Epidemiological research aims to investigate how multiple exposures affect health outcomes of interest, but observational studies often suffer from biases caused by unmeasured confounders. In this study, we develop a novel sensitivity model...