Bayesian Federated Inference for regression models based on non-shared medical center data [0.03%]
基于非共享医疗中心数据的回归模型的贝叶斯联合推理方法
Marianne A Jonker,Hassan Pazira,Anthony C C Coolen
Marianne A Jonker
To estimate accurately the parameters of a regression model, the sample size must be large enough relative to the number of possible predictors for the model. In practice, sufficient data is often lacking, which can lead to overfitting of t...
Meta-analytic rain cloud plots: Improving evidence communication through data visualization design principles [0.03%]
基于元分析的雨云图：通过数据可视化设计原则改进证据传播
Kaitlyn G Fitzgerald,David Khella,Avery Charles et al.
Kaitlyn G Fitzgerald et al.
Results of meta-analyses are of interest not only to researchers but often to policy-makers and other decision-makers (e.g., in education and medicine), and visualizations play an important role in communicating data and statistical evidenc...
A comprehensive systematic review dataset is a rich resource for training and evaluation of AI systems for title and abstract screening [0.03%]
全面的系统评价数据集是用于训练和评估文献题录筛查AI系统的宝贵资源
Gary C K Chan,Estrid He,Janni Leung et al.
Gary C K Chan et al.
When conducting a systematic review, screening the vast body of literature to identify the small set of relevant studies is a labour-intensive and error-prone process. Although there is an increasing number of fully automated tools for scre...
Yu-Lun Liu,Bingyu Zhang,Haitao Chu et al.
Yu-Lun Liu et al.
Network meta-analysis (NMA), also known as mixed treatment comparison meta-analysis or multiple treatments meta-analysis, extends conventional pairwise meta-analysis by simultaneously synthesizing multiple interventions in a single integrat...
Bayesian Federated Inference for regression models based on non-shared medical center data - ERRATUM [0.03%]
基于非共享医疗中心数据的回归模型的贝叶斯联合推理的勘误表
Marianne A Jonker,Hassan Pazira,Anthony C C Coolen
Marianne A Jonker
Published Erratum
Research synthesis methods. 2025 Mar;16(2):424. DOI:10.1017/rsm.2025.23 2025
Effect modification and non-collapsibility together may lead to conflicting treatment decisions: A review of marginal and conditional estimands and recommendations for decision-making [0.03%]
效应修饰和非坍缩性可能会导致相互冲突的治疗决策：边际和条件估计指标以及决策制定建议综述
David M Phillippo,Antonio Remiro-Azócar,Anna Heath et al.
David M Phillippo et al.
Effect modification occurs when a covariate alters the relative effectiveness of treatment compared to control. It is widely understood that, when effect modification is present, treatment recommendations may vary by population and by subgr...
ZIBGLMM: Zero-inflated bivariate generalized linear mixed model for meta-analysis with double-zero-event studies [0.03%]
具有双重零事件研究的元分析的零膨胀双变量广义线性混合模型(ZIBGLMM)
Lu Li,Lifeng Lin,Joseph C Cappelleri et al.
Lu Li et al.
Double-zero-event studies (DZS) pose a challenge for accurately estimating the overall treatment effect in meta-analysis (MA). Current approaches, such as continuity correction or omission of DZS, are commonly employed, yet these ad hoc met...
Methods for information-sharing in network meta-analysis: Implications for inference and policy [0.03%]
网络meta分析的信息共享方法：对推论和政策的影响
Georgios F Nikolaidis,Beth Woods,Stephen Palmer et al.
Georgios F Nikolaidis et al.
Limited evidence on relative effectiveness is common in Health Technology Assessment (HTA), often due to sparse evidence on the population of interest or study-design constraints. When evidence directly relating to the policy decision is li...
CausalMetaR: An R package for performing causally interpretable meta-analyses [0.03%]
因果元分析R包CausalMetaR：用于执行可解释因果关系的元分析
Guanbo Wang,Sean McGrath,Yi Lian
Guanbo Wang
Researchers would often like to leverage data from a collection of sources (e.g., meta-analyses of randomized trials, multi-center trials, pooled analyses of observational cohorts) to estimate causal effects in a target population of intere...
CausalMetaR: An R package for performing causally interpretable meta-analyses - ERRATUM [0.03%]
CausalMetaR：一个可进行因果推断的元分析的R包-勘误表
Guanbo Wang,Sean McGrath,Yi Lian
Guanbo Wang
Published Erratum
Research synthesis methods. 2025 Mar;16(2):441. DOI:10.1017/rsm.2025.22 2025