ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE [0.03%]
考虑集群内效应异质性和干扰的时变因果效应调节评估方法研究
Jieru Shi,Zhenke Wu,Walter Dempsey
Jieru Shi
The micro-randomized trial (MRT) is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points. ...
Marginal proportional hazards models for multivariate interval-censored data [0.03%]
多元区间删失数据的边际比例危险模型
Yangjianchen Xu,Donglin Zeng,D Y Lin
Yangjianchen Xu
Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval...
Molei Liu,Eugene Katsevich,Lucas Janson et al.
Molei Liu et al.
We consider the problem of conditional independence testing: given a response Y and covariates (X,Z), we test the null hypothesis that Y⫫X∣Z. The conditional randomization test was recently proposed as a way to use distribution...
A multiplicative structural nested mean model for zero-inflated outcomes [0.03%]
一种用于零膨胀结果的乘法结构嵌套均值模型
Miao Yu,Wenbin Lu,Shu Yang et al.
Miao Yu et al.
Zero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes which flexib...
Sample-constrained partial identification with application to selection bias [0.03%]
基于样本约束的部分识别及其在偏倚修正中的应用
Matthew J Tudball,Rachael A Hughes,Kate Tilling et al.
Matthew J Tudball et al.
Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference...
Gradient-based sparse principal component analysis with extensions to online learning [0.03%]
基于梯度的稀疏主元分析及其在线学习扩展方法
Yixuan Qiu,Jing Lei,Kathryn Roeder
Yixuan Qiu
Sparse principal component analysis is an important technique for simultaneous dimensionality reduction and variable selection with high-dimensional data. In this work we combine the unique geometric structure of the sparse principal compon...
Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring [0.03%]
具有相关删失的生存结果多阶段最优动态治疗方案
Hunyong Cho,Shannon T Holloway,David J Couper et al.
Hunyong Cho et al.
We propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent o...
Marina Bogomolov,Christine B Peterson,Yoav Benjamini et al.
Marina Bogomolov et al.
We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We descri...
Testing generalized linear models with high-dimensional nuisance parameter [0.03%]
高维 nuisance 参数下的广义线性模型的检验
Jinsong Chen,Quefeng Li,Hua Yun Chen
Jinsong Chen
Generalized linear models often have a high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a ...
Instrumental variable estimation of the marginal structural Cox model for time-varying treatments [0.03%]
时间依赖性治疗因素的边缘结构Cox模型的工具变量估计方法研究
Y Cui,H Michael,F Tanser et al.
Y Cui et al.
Robins (1998) introduced marginal structural models, a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established th...