将随机试验中个体的因果推断推广到所有符合试验条件的个体。
Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals.
作者信息
Dahabreh Issa J, Robertson Sarah E, Tchetgen Eric J, Stuart Elizabeth A, Hernán Miguel A
机构信息
Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, Rhode Island.
Departments of Health Services, Policy & Practice and Epidemiology, Brown University, Providence, Rhode Island.
出版信息
Biometrics. 2019 Jun;75(2):685-694. doi: 10.1111/biom.13009. Epub 2019 Jun 21.
Abstract
We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite-sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial-eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.
摘要
我们考虑在符合试验条件个体的队列中进行的随机试验中的因果推断方法，包括未被随机分组的个体。我们展示了如何利用来自整个队列的基线协变量数据，以及仅来自随机分组个体的治疗和结局数据，来识别所有符合条件个体的目标人群中的潜在（反事实）结局均值和平均治疗效果。我们回顾了可识别性条件，提出了估计量，并在模拟研究中评估了估计量的有限样本性能。作为一个示例，我们将这些估计量应用于一个嵌套在符合试验条件个体队列中的试验，以比较冠状动脉旁路移植术加药物治疗与单纯药物治疗对慢性冠状动脉疾病的疗效。
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Model misspecification and bias for inverse probability weighting estimators of average causal effects.
Biom J. 2023 Feb;65(2):e2100118. doi: 10.1002/bimj.202100118. Epub 2022 Aug 31.
2
On Inverse Probability Weighting for Nonmonotone Missing at Random Data.
J Am Stat Assoc. 2018;113(521):369-379. doi: 10.1080/01621459.2016.1256814. Epub 2017 Dec 1.
3
Reflection on modern methods: selection bias-a review of recent developments.
Int J Epidemiol. 2018 Oct 1;47(5):1714-1722. doi: 10.1093/ije/dyy138.
4
Robust estimation of encouragement-design intervention effects transported across sites.
J R Stat Soc Series B Stat Methodol. 2017 Nov;79(5):1509-1525. doi: 10.1111/rssb.12213. Epub 2016 Oct 31.
5
Randomized, Controlled Trials in Health Insurance Systems.
N Engl J Med. 2017 Sep 7;377(10):957-964. doi: 10.1056/NEJMra1510058.
6
Using group data to treat individuals: understanding heterogeneous treatment effects in the age of precision medicine and patient-centred evidence.
Int J Epidemiol. 2016 Dec 1;45(6):2184-2193. doi: 10.1093/ije/dyw125.
7
Pragmatic Trials.
N Engl J Med. 2016 Aug 4;375(5):454-63. doi: 10.1056/NEJMra1510059.
8
Integrating Randomized Comparative Effectiveness Research with Patient Care.
N Engl J Med. 2016 Jun 2;374(22):2152-8. doi: 10.1056/NEJMra1510057.
9
New methods for treatment effect calibration, with applications to non-inferiority trials.
Biometrics. 2016 Mar;72(1):20-9. doi: 10.1111/biom.12388. Epub 2015 Sep 13.
10
Variance reduction in randomised trials by inverse probability weighting using the propensity score.
Stat Med. 2014 Feb 28;33(5):721-37. doi: 10.1002/sim.5991. Epub 2013 Sep 30.
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