.png){kind=icon}
返回
Targeted maximum likelihood estimation in safety analysis
.png){kind=icon}
OA
AI
DOI：10.1016/j.jclinepi.2013.02.017.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Objectives: To compare the performance of a targeted maximum likelihood estimator (TMLE) and a collaborative TMLE (CTMLE) to other estimators in a drug safety analysis, including a regression-based estimator, propensity score (PS) based estimators, and an alternate doubly robust (DR) estimator in a real example and simulations. Study Design and Setting: The real data set is a subset of observational data from Kaiser Permanente Northern California formatted for use in active drug safety surveillance. Both the real and simulated data sets include potential confounders, a treatment variable indicating use of one of two antidiabetic treatments and an outcome variable indicating occurrence of an acute myocardial infarction (AMI). Results: In the real data example, there is no difference in AMI rates between treatments. In simulations, the double robustness property is demonstrated: DR estimators are consistent if either the initial outcome regression or PS estimator is consistent, whereas other estimators are inconsistent if the initial estimator is not consistent. In simulations with near-positivity violations, CTMLE performs well relative to other estimators by adaptively estimating the PS. Conclusion: Each of the DR estimators was consistent, and TMLE and CTMLE had the smallest mean squared error in simulations. (C) 2013 Elsevier Inc. All rights reserved.
Keyword:
Safety analysis
Targeted maximum likelihood estimation
Doubly robust
Causal inference
Collaborative targeted maximum likelihood estimation
Super learning
期刊
IF:
5.2
论文数:
8.5K
被引数:
4.3W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏Treatment Effects in the Presence of Unmeasured Confounding: Dealing With Observations in the Tails of the Propensity Score Distribution-A Simulation Study在存在无法测量的混杂因素的情况下的治疗效果: 处理倾向得分分布尾部的观察结果-一项模拟研究
OAAI
OAAI分享收藏
查看更多内容