Testing for treatment effect twice using internal and external controls in clinical trials [0.03%]
临床试验中利用内外对照两次检验治疗效应
Yanyao Yi,Ying Zhang,Yu Du et al.
Yanyao Yi et al.
Leveraging external controls - relevant individual patient data under control from external trials or real-world data - has the potential to reduce the cost of randomized controlled trials (RCTs) while increasing the proportion of trial pat...
An approach to nonparametric inference on the causal dose-response function [0.03%]
一种对因果剂量反应函数进行非参数推断的方法研究
Aaron Hudson,Elvin H Geng,Thomas A Odeny et al.
Aaron Hudson et al.
The causal dose-response curve is commonly selected as the statistical parameter of interest in studies where the goal is to understand the effect of a continuous exposure on an outcome. Most of the available methodology for statistical inf...
Assessing surrogate heterogeneity in real world data using meta-learners [0.03%]
利用元学习器评估真实世界数据中的替代物异质性
Rebecca Knowlton,Layla Parast
Rebecca Knowlton
Surrogate markers are most commonly studied within the context of randomized clinical trials. However, the need for alternative outcomes also extends to real-world public health and social science research, where randomized trials are often...
Estimating average causal effects with incomplete exposure and confounders [0.03%]
利用不完整暴露和混杂因素估计平均因果效应
Lan Wen,Glen McGee
Lan Wen
Standard methods for estimating average causal effects require complete observations of the exposure and confounders. In observational studies, however, missing data are ubiquitous. Motivated by a study on the effect of prescription opioids...
Semiparametric discovery and estimation of interaction in mixed exposures using stochastic interventions [0.03%]
半参数发现和估计混合暴露的交互作用的随机干预方法
David B McCoy,Alan Hubbard,Mark van der Laan et al.
David B McCoy et al.
Understanding the complex interactions among multiple environmental exposures is critical for assessing their combined impact on health outcomes. This study introduces InterXshift, a novel semiparametric method that provides a nonparametric...
Bridging binarization: causal inference with dichotomized continuous exposures [0.03%]
连续性处理变量的二值化：因果推断中的桥梁方法
Kaitlyn Lee,Alan Hubbard,Alejandro Schuler
Kaitlyn Lee
The average treatment effect (ATE) is a common parameter estimated in causal inference literature, but it is only defined for binary exposures. Thus, despite concerns raised by some researchers, many studies seeking to estimate the causal e...
Discovery of critical thresholds in mixed exposures and estimation of policy intervention effects [0.03%]
混合暴露的关键阈值的发现及政策干预效应的估计
David B McCoy,Alan Hubbard,Mark van der Laan et al.
David B McCoy et al.
Regulations of chemical exposures often focus on individual substances, neglecting the amplified toxicity that can arise from multiple concurrent exposures. We propose a novel methodology to identify critical thresholds in multivariate expo...
Generalized coarsened confounding for causal effects: a large-sample framework [0.03%]
因果效应的广义降质混杂：一个大样本框架
Debashis Ghosh,Lei Wang
Debashis Ghosh
There has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider a class of generalized coarsened procedures for confounding. At a ...
Stijn Vansteelandt,Paweł Morzywołek
Stijn Vansteelandt
Orthogonal meta-learners, such as DR-learner (Kennedy EH. Towards optimal doubly robust estimation of heterogeneous causal effects. arXiv preprint arXiv:2004.14497 2020), R-learner (Nie X, Wager S. Quasi-oracle estimation of heterogeneous t...
Ting Ye,Qijia He,Shuxiao Chen et al.
Ting Ye et al.
In an observational study, it is common to leverage known null effects to detect bias. One such strategy is to set aside a placebo sample - a subset of data immune from the hypothesized cause-and-effect relationship. Existence of an effect ...