对接受治疗者和未接受治疗者的平均治疗效果进行G计算。
G-computation of average treatment effects on the treated and the untreated.
作者信息
Wang Aolin, Nianogo Roch A, Arah Onyebuchi A
机构信息
Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
California Center for Population Research (CCPR), Los Angeles, CA, USA.
出版信息
BMC Med Res Methodol. 2017 Jan 9;17(1):3. doi: 10.1186/s12874-016-0282-4.
BACKGROUND
Average treatment effects on the treated (ATT) and the untreated (ATU) are useful when there is interest in: the evaluation of the effects of treatments or interventions on those who received them, the presence of treatment heterogeneity, or the projection of potential outcomes in a target (sub-) population. In this paper we illustrate the steps for estimating ATT and ATU using g-computation implemented via Monte Carlo simulation.
METHODS
To obtain marginal effect estimates for ATT and ATU we used a three-step approach: fitting a model for the outcome, generating potential outcome variables for ATT and ATU separately, and regressing each potential outcome variable on treatment intervention.
RESULTS
The estimates for ATT, ATU and average treatment effect (ATE) were of similar magnitude, with ATE being in between ATT and ATU as expected. In our illustrative example, the effect (risk difference [RD]) of a higher education on angina among the participants who indeed have at least a high school education (ATT) was -0.019 (95% CI: -0.040, -0.007) and that among those who have less than a high school education in India (ATU) was -0.012 (95% CI: -0.036, 0.010).
CONCLUSIONS
The g-computation algorithm is a powerful way of estimating standardized estimates like the ATT and ATU. Its use should be encouraged in modern epidemiologic teaching and practice.
背景
当人们关注以下方面时，对接受治疗者的平均治疗效果（ATT）和未接受治疗者的平均治疗效果（ATU）很有用：评估治疗或干预措施对接受者的效果、治疗异质性的存在，或目标（亚）人群中潜在结果的预测。在本文中，我们阐述了使用通过蒙特卡罗模拟实现的g计算来估计ATT和ATU的步骤。
方法
为了获得ATT和ATU的边际效应估计值，我们采用了三步法：拟合结果模型、分别生成ATT和ATU的潜在结果变量，以及将每个潜在结果变量对治疗干预进行回归。
结果
ATT、ATU和平均治疗效果（ATE）的估计值大小相似，正如预期的那样，ATE介于ATT和ATU之间。在我们的示例中，在印度确实至少受过高中教育的参与者中，高等教育对心绞痛的影响（风险差[RD]）（ATT）为-0.019（95%CI：-0.040，-0.007），而在受教育程度低于高中的参与者中（ATU）为-0.012（95%CI：-0.036，0.010）。
结论
g计算算法是估计ATT和ATU等标准化估计值的有力方法。应在现代流行病学教学和实践中鼓励使用该方法。
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