Adaptive sample size re-estimation designs for a two-stage randomized trial with binary outcome [0.03%]
具有二元结果的自适应样本量再估计的两阶段随机试验设计
Zhixin Tang,Guogen Shan
Zhixin Tang
A parallel randomized trial is frequently used to investigate the treatment effectiveness as compared to the gold standard. In early phase trials, a group sequential design has the potential to reduce the expected sample size as compared to...
Informative simultaneous confidence intervals for graphical test procedures [0.03%]
用于图形检验程序的具说明性的同时置信区间
Werner Brannath,Liane Kluge,Martin Scharpenberg
Werner Brannath
Simultaneous confidence intervals that are compatible with a given closed test procedure are often non-informative. More precisely, for a one-sided null hypothesis, the bound of the simultaneous confidence interval can stick to the border o...
A jackknife approach to estimate the prediction uncertainty from binary classifiers under right-censoring [0.03%]
一种用于估计右删失下二分类预测不确定性的刀切法
Antje Jahn-Eimermacher,Lukas Klein,Gunter Grieser
Antje Jahn-Eimermacher
Clinical prediction models are developed to estimate a patient's risk for a specific outcome, and machine learning is frequently employed to improve prediction accuracy. When the outcome is some event that happens over time, binary classifi...
Latent classification of time-dependent transition rates in longitudinal binary outcome data [0.03%]
纵向二值结果数据中时间依赖性转换速率的潜在分类方法研究
Joonha Chang,Wenyaw Chan
Joonha Chang
Continuous-time Markov chain (CTMC) models and latent classification methods are commonly used to analyze longitudinal categorical outcomes in medical research. While CTMC models are popular for their simplicity and effectiveness, their ass...
Using inverse probability of censoring weighting to estimate hypothetical estimands in clinical trials: Should we implement stabilisation, and if so how? [0.03%]
在临床试验中使用逆概率审查加权估计假设估算量：我们应该实施稳定化吗？如果是，该如何实施？
Jingyi Xuan,Shahrul Mt-Isa,Nicholas R Latimer et al.
Jingyi Xuan et al.
Inverse probability of censoring weighting is an approach used to estimate the hypothetical treatment effect that would have been observed in a clinical trial if certain intercurrent events had not occurred. Despite the unbiased estimates o...
Comparative study of Bayesian and frequentist methods for epidemic forecasting: Insights from simulated and historical data [0.03%]
基于模拟数据和历史数据的流行病预测的贝叶斯方法与频率学派方法比较研究
Hamed Karami,Ruiyan Luo,Pejman Sanaei et al.
Hamed Karami et al.
Accurate epidemic forecasting is critical for effective public health interventions. This study compares Bayesian and Frequentist estimation frameworks within deterministic compartmental epidemic models, focusing on nonlinear least squares ...
Two-stage Bayesian network meta-analysis of individualized treatment rules for multiple treatments with siloed data [0.03%]
基于隔绝数据的多重治疗个体化治疗方案的两阶段贝叶斯网络meta分析研究
Junwei Shen,Erica Em Moodie,Shirin Golchi
Junwei Shen
Individualized treatment rules leverage patient-level information to tailor treatments for individuals. Estimating these rules, with the goal of optimizing expected patient outcomes, typically relies on individual-level data to identify the...
Center-specific causal inference with multicenter trials-Interpreting trial evidence in the context of each participating center [0.03%]
基于多中心试验的中心特异性因果推断——在每个参与中心背景下解读试验证据
Sarah E Robertson,Jon A Steingrimsson,Nina R Joyce et al.
Sarah E Robertson et al.
In multicenter randomized trials, when effect modifiers have a different distribution across centers, comparisons between treatment groups that average (standardize) effects over centers may not apply to any of the populations underlying th...
Erik van Zwet,Witold Wiȩcek,Andrew Gelman
Erik van Zwet
Effect sizes typically vary among studies of the same intervention. In a random effects meta-analysis, this source of variation is taken into account, at least to some extent. However, when we have only one study, the heterogeneity remains ...
Assessing spillover effects: Handling missing outcomes in network-based studies [0.03%]
网络化研究中的外溢效应评估：处理结果缺失问题
TingFang Lee,Ashley L Buchanan,Natallia Katenka et al.
TingFang Lee et al.
Estimating causal effects in the presence of spillover among individuals within a social network poses challenges due to missing information. Spillover effects refer to the impact of an intervention on individuals not directly exposed thems...