网络meta分析（Network meta-analysis, NMA）是在单一meta分析中同时比较多种干预措施的有用工具，当研究旨在从几种有效候选方案中找出最佳疗法时，它对医疗决策非常有帮助。然而，其结果的有效性受到发表偏倚问题的威胁。现有用于标准成对meta分析中发表偏倚问题的方法，由于NMA复杂的数据结构和数据合并的潜在假设，很难扩展到该领域。本文中，我们旨在提供一个灵活的逆概率加权（inverse probability weighting, IPW）框架，结合几种t型选择函数来处理NMA场景下的发表偏倚问题。为求解这些提出的选择函数，我们建议利用来自多个临床试验注册库的未发表研究的额外信息。一项全面的数值研究和一个真实案例表明，我们的方法有助于获得更准确的估计值和更高的覆盖概率，并改善NMA的其他特性（例如干预措施排序）。
英文摘要：
Network meta-analysis (NMA) is a useful tool to compare multiple interventions simultaneously in a single meta-analysis, it can be very helpful for medical decision making when the study aims to find the best therapy among several active candidates. However, the validity of its results is threatened by the publication bias issue. Existing methods to handle the publication bias issue in the standard pairwise meta-analysis are hard to extend to this area with the complicated data structure and the underlying assumptions for pooling the data. In this paper, we aimed to provide a flexible inverse probability weighting (IPW) framework along with several t-type selection functions to deal with the publication bias problem in the NMA context. To solve these proposed selection functions, we recommend making use of the additional information from the unpublished studies from multiple clinical trial registries. A comprehensive numerical study and a real example showed that our methodology can help obtain more accurate estimates and higher coverage probabilities, and improve other properties of an NMA (e.g., ranking the interventions).
A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications
暴露和结果变量的联合错误分类可能导致因果关系确定-结果效应的流行病学研究中出现相当大的偏倚。在本文中，我们提出了一个新的极大似然估计的边际因果比值比，同时调整混杂和几种形式的联合误分类的暴露和结果变量。所提出的方法依赖于验证数据的建设的权重，占两个来源的偏见。加权估计量是Gravel和Platt（Statistics in Medicine，2018）提出的暴露误分类加权估计量的扩展，适用于再梗死数据。进行了模拟研究，研究其有限样本的性质，并将其与不考虑混杂或误分类的方法进行比较。新的估计显示出有利的大样本性能的模拟。需要进一步研究所提出的方法的敏感性，以及违反其假设的替代方案。在现有的R包中的一个新的R函数的估计的实施是方便的。
Covariate-adjusted win statistics in randomized clinical trials with ordinal outcomes
Shenzhen Technology University（深圳理工大学）；Feinberg School of Medicine, Northwestern University（西北大学费恩伯格医学院）；University of Wisconsin - Madison（威斯康星大学麦迪逊分校）；Yale University School of Public Health（耶鲁大学公共卫生学院）；Yale Center for Analytical Sciences, Yale University School of Public Health（耶鲁大学公共卫生学院分析科学中心）；University of Washington（华盛顿大学）；Clinical Learning, Evidence, and Research (CLEAR) Center, University of Washington（华盛顿大学临床学习、证据与研究（CLEAR）中心）；Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine（耶鲁医学院内科学系心血管医学部）；Cardiovascular Medicine Analytics Center, Yale School of Medicine（耶鲁医学院心血管医学分析中心）；Center for Methods in Implementation and Prevention Science, Yale School of Public Health（耶鲁大学公共卫生学院实施与预防科学方法中心）
Weighting a Census as a Non-Probability Sample: A Doubly Robust Framework for Correcting Differential Undercoverage in Uruguay's 2023 Census
Instituto Nacional de Estadística, Uruguay（乌拉圭国家统计研究所）；Facultad de Ciencias Económicas y de Administración, Universidad de la República, Uruguay（共和国大学经济与管理学院）
Causal Inference with Multiple Misclassified Exposures: A Control Variate-Adjusted Calibration Weighting Approach
Colorado School of Public Health（科罗拉多公共卫生学院）；Johns Hopkins Bloomberg School of Public Health（约翰斯·霍普金斯大学彭博公共卫生学院）；University of Colorado School of Medicine（科罗拉多大学医学院）；Colorado State University（科罗拉多州立大学）
Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment
School of Population and Public Health, University of British Columbia（英属哥伦比亚大学公共卫生与人口健康学院）
