Clustered competing risks data are commonly encountered in multicenter studies. The analysis of such data is often complicated due to informative cluster size, a situation where the outcomes under study are associated with the size of the cluster. In addition, cause of failure is frequently incompletely observed in real-world settings. To the best of our knowledge, there is no methodology for population-averaged analysis with clustered competing risks data with informative cluster size and missing causes of failure. To address this problem, we consider the semiparametric marginal proportional cause-specific hazards model and propose a maximum partial pseudolikelihood estimator under a missing at random assumption. To make the latter assumption more plausible in practice, we allow for auxiliary variables that may be related to the probability of missingness. The proposed method does not impose assumptions regarding the within-cluster dependence and allows for informative cluster size. The asymptotic properties of the proposed estimators for both regression coefficients and infinite-dimensional parameters, such as the marginal cumulative incidence functions, are rigorously established. Simulation studies show that the proposed method performs well and that methods that ignore the within-cluster dependence and the informative cluster size lead to invalid inferences. The proposed method is applied to competing risks data from a large multicenter HIV study in sub-Saharan Africa where a significant portion of causes of failure is missing.
Semiparametric clustered overdispersed multinomial goodness-of-fit of log-linear models
传统上，狄利克雷-多项分布被认为是整群抽样方案生成列联表的关键模型。然而，还有其他可能的分布适合这些列联表。本文介绍了新的检验统计量能够测试对数线性建模假设，没有分布规格，当集群的个人可能是均匀相关的。Alonso-Revenga et al.（2016）提出的群内相关系数估计量适用于不同的聚类大小，在拟合优度检验统计量的构建中起着至关重要的作用。
Semiparametric Regression for Misclassified Competing Risks Data
Department of Biostatistics and Health Data Science, Fairbanks School of Public Health and School of Medicine, Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校费尔班克斯公共卫生学院和医学院生物统计学与健康数据科学系）；Department of Epidemiology and Biostatistics, CUNY Graduate School of Public Health and Health Policy, City University of New York（纽约市立大学流行病学与生物统计系，CUNY公共健康与卫生政策研究生院）
Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risks Data: With Applications to Massive Biobank Data
Addressing the Influence of Unmeasured Confounding in Observational Studies with Time-to-Event Outcomes: A Semiparametric Sensitivity Analysis Approach
Doubly Robust Estimation under Covariate Dependent Censoring in Semi-Competing Risks Data
Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego（加州大学圣地亚哥分校赫伯特·韦特海姆公共卫生与人类长寿科学学院）；Department of Mathematics, University of California, San Diego（加州大学圣地亚哥分校数学系）；Halicioglu Data Science Institute, University of California, San Diego（加州大学圣地亚哥分校哈利奥格鲁数据科学研究所）
Conditional copula graphic estimator for semi-competing risks data
Children’s Hospital Research Institute of Manitoba, University of Manitoba（曼尼托巴儿童医院研究所，曼尼托巴大学）；Department of Mathematics and Statistics, University of Guelph（圭尔夫大学数学与统计系）；Hospital for Sick Children（多伦多多伦多儿童医院）；Department of Statistics, University of Manitoba（曼尼托巴大学统计系）
Doubly Robust Machine Learning for Population Size Estimation with Missing Covariates: Application to Gaza Conflict Mortality
Center for Data Science New York University（纽约大学数据科学中心）；Department of Statistics & Data Science Carnegie Mellon University（卡内基梅隆大学统计与数据科学系）；Department of Medical Statistics London School of Hygiene and Tropical Medicine（伦敦卫生与热带医学院医学统计学系）
