调查常被用于推动流行病学、健康学以及社会与行为科学领域的研究。通常这类调查并非简单随机抽样，受访者会被赋予反映其入选调查概率的权重。众所周知，分析人员可利用这些调查权重生成总体总量等总体统计量的无偏估计。本文中，我们证明在将数据划分为训练集和测试集时，调查权重对评估预测模型的质量同样有益。具体而言，我们将灵敏度、特异度等模型评估统计量定义为有限总体统计量，并使用由原始数据随机子集构成的样本测试集，计算这些统计量的调查加权估计值。通过使用来自National Survey on Drug Use and Health（全国药物使用与健康调查）和National Comorbidity Survey（全国共病调查）的数据开展模拟研究，我们发现利用样本测试集估计的未加权指标会错误反映总体性能，但加权指标可针对复杂抽样设计进行适当调整。我们还证明，对于采用上采样缓解类别不平衡训练得到的模型，这一结论同样成立。研究结果表明，在评估样本测试集上的性能时应使用加权指标。
英文摘要：
Surveys are commonly used to facilitate research in epidemiology, health, and the social and behavioral sciences. Often, these surveys are not simple random samples, and respondents are given weights reflecting their probability of selection into the survey. It is well known that analysts can use these survey weights to produce unbiased estimates of population quantities like totals. In this article, we show that survey weights also can be beneficial for evaluating the quality of predictive models when splitting data into training and test sets. In particular, we characterize model assessment statistics, such as sensitivity and specificity, as finite population quantities, and compute survey-weighted estimates of these quantities with sample test data comprising a random subset of the original data.Using simulations with data from the National Survey on Drug Use and Health and the National Comorbidity Survey, we show that unweighted metrics estimated with sample test data can misrepresent population performance, but weighted metrics appropriately adjust for the complex sampling design. We also show that this conclusion holds for models trained using upsampling for mitigating class imbalance. The results suggest that weighted metrics should be used when evaluating performance on sample test data.
Evaluating treatment effects on longitudinal outcomes with attrition due to death: Methods for a two-dimentional estimand with a case study in Quality of Life
Ghent University（根特大学）；Leiden University Medical Center（莱顿大学医学中心）；Pfizer Inc（辉瑞公司）；Oslo University Hospital（奥斯陆大学医院）；European Organisation for Research and Treatment of Cancer (EORTC) Headquarters（欧洲癌症研究与治疗组织（EORTC）总部）；University of Freiburg（弗赖堡大学）
An Online Meta-Level Adaptive Design Framework with Targeted Learning Inference: Applications to Evaluating and Utilizing Surrogate Outcomes in Adaptive Designs
Division of Biostatistics, University of California, Berkeley（加州大学伯克利分校生物统计系）；Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center（弗雷德·哈钦森癌症中心疫苗与传染病部）；Department of Biostatistics, University of Washington（华盛顿大学生物统计系）
Evaluating Surrogates in Individualized Treatment Rules
School of Statistics, Renmin University of China（中国人民大学统计学院）；School of Economics and Management, Tsinghua University（清华大学经济管理学院）；Center for Applied Statistics and School of Statistics, Renmin University of China（中国人民大学应用统计科学中心及统计学院）
A Causal Inference Approach for Evaluating Diagnostic Tests and AI-Enabled Medical Devices: From Effect Modification to Information-Augmented Decision-Making
Division of Biostatistics, University of California, Berkeley（加州大学伯克利分校生物统计系）
A causal framework for evaluating the total effect of strategies aiming to expand screening and to improve outcomes
University of California, Berkeley（加州大学伯克利分校）；Kenya Medical Research Institute (KEMRI)（肯尼亚医学研究所）；Infectious Disease Research Collaboration (IDRC)（传染病研究合作组织）；Makerere University（Makerere大学）；University of California, San Francisco（加州大学旧金山分校）
Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based vector-on-matrix regression
