Constructing G-computation Estimators: Two Case Studies in Selection Bias [0.03%]
G-公式估算器的构建：选择偏倚的两个案例研究
Paul N Zivich,Haidong Lu
Paul N Zivich
G-computation is a useful estimation method that can be adapted to address various biases in epidemiology. However, these adaptations may not be obvious for some complex causal structures. This challenge is an example of the much wider issu...
Arvid Sjölander,Iuliana Ciocănea-Teodorescu,Erin E Gabriel
Arvid Sjölander
Unmeasured confounding is an important obstacle when estimating causal effects from observational data. Ding and VanderWeele (EPIDEMIOLOGY 2016;27:368) derived bounds for causal effects, based on sensitivity parameters that quantify the max...
How Should We Study the Indirect Effects of Antimicrobial Treatment Strategies?: A Causal Perspective [0.03%]
我们应该如何研究抗菌治疗策略的间接效应？一种因果关系视角
Juan Gago,Christopher Boyer,Marc Lipsitch
Juan Gago
Effective antimicrobial stewardship requires unbiased assessment of the benefits and harms of different treatment strategies, considering both immediate patient outcomes and patterns of antimicrobial resistance. In principle, these benefits...
Transporting Results from a Trial to an External Target Population When Trial Participation Impacts Adherence [0.03%]
当试验参与影响依从性时，如何将试验结果应用于外部目标人群
Rachael K Ross,Iván Díaz,Amy J Pitts et al.
Rachael K Ross et al.
Randomized clinical trials are considered the gold standard for informing treatment guidelines, but results may not generalize to real-world populations. Generalizability is hindered by distributional differences in baseline covariates and ...
Sonja A Swanson
Sonja A Swanson
Causal identification conditions for the effect of treatment in the treated: Illustration using the Northwest Germany Stroke Registry [0.03%]
利用西北德国卒中登记数据探讨仅接受某种治疗的患者的因果效应识别条件
Catherine Wiener,Paul N Zivich,Tobias Kurth et al.
Catherine Wiener et al.
Background: A set of conditions sufficient to identify the average treatment effect (ATE) in observational data includes no measurement error, causal consistency, and conditional mean exchangeability with positivity. The ...
Timothy L Lash
Timothy L Lash
Unbiased estimates using temporally aggregated outcome data in time series analysis: generalization to different outcomes, exposures and types of aggregation [0.03%]
时间序列分析中使用不同结果、暴露和聚合类型的暂态聚集结果数据进行无偏估计的推广研究
Xavier Basagaña,Joan Ballester
Xavier Basagaña
Background: A new method for time series analysis was recently formulated and implemented that uses temporally aggregated outcome data to generate unbiased estimates of the underlying association between temporally disagg...
Causal mediation analysis with mediator-outcome confounders affected by exposure - on definition and identification of generalized natural indirect effect [0.03%]
暴露影响中介变量与结局相关混杂因素的因果中介分析——关于广义自然间接效应的定义及识别问题研究
Yan-Lin Chen,Tsung Yu,Sheng-Hsuan Lin
Yan-Lin Chen
Causal mediation analysis aims to disentangle the pathways through which an exposure influences an outcome. In the presence of mediator-outcome confounders affected by exposure (intermediate confounders), the natural indirect effect (NIE) i...
Does Delayed Response Due to Busy Ambulances Impact Risk of Death and Hospital Service Use?: A Cohort Study of 240,000 Medical Emergencies [0.03%]
救护车忙是否会影响延迟救治及死亡风险：一项针对24万医疗急救的队列研究
Andreas Asheim,Lars Eide Næss,Andreas Krüger et al.
Andreas Asheim et al.
Objectives: When ground ambulances are busy with any task, delays are likely for concurrent emergencies. Whereas time-critical conditions are affected by delays, general impacts remain unclear. We aimed to assess how dela...