G-computation of average treatment effects on the treated and the untreated

BMC Medical Research Methodology, Jan 2017

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.

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G-computation of average treatment effects on the treated and the untreated

Wang et al. BMC Medical Research Methodology G-computation of average treatment effects on the treated and the untreated Aolin Wang 0 1 Roch A. Nianogo 0 1 Onyebuchi A. Arah 0 1 2 0 California Center for Population Research (CCPR) , Los Angeles, CA , USA 1 Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA) , Los Angeles, CA , USA 2 UCLA Center for Health Policy Research , Los Angeles, CA , USA 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. Average treatment effects on the treated (ATT); Average treatment effects on the untreated (ATU); G-computation; Parametric g-formula; Resampling; Simulation - Background In epidemiology, (bio)statistics and related fields, researchers are often interested in the average treatment effect in the total population (average treatment effect, ATE). This quantity provides the average difference in outcome between units assigned to the treatment and units assigned to the placebo (control) [1]. However, in economics and evaluation studies, it has been noted that the average treatment effect among units who actually receive the treatment or intervention (average treatment effects on the treated, ATT) may be the implicit quantity sought and the most relevant to policy makers [2]. For instance, consider a scenario where a government has implemented a smoking cessation campaign intervention to decrease the smoking prevalence in a city and now wishes to evaluate the impact of such intervention. Although the overarching goal of such evaluation may be to assess the impact of such intervention in reducing the prevalence of smoking in the general population (i.e. ATE), researchers and policymakers might be interested in explicitly evaluating the effect of the intervention on those who actually received the intervention (i.e. ATT) but not that on those among whom the intervention was never intended. Alternatively, researchers may be interested in estimating the potential impact of an existing program in a new target (sub-) population. For instance, one might wish to project the effect of the smoking cessation intervention in a city that did not receive the intervention in order to gauge its potential impact when such intervention is actually implemented. This latter quantity is referred to as the average treatment effect on the untreated (ATU). Interestingly, the ATE can be seen as a weighted average of the ATT and the ATU. All three quantities will be © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. equal when the covariate distribution is the same among the treated and the untreated (e.g. under perfect randomization with perfect compliance or when there is no unmeasured confounders) and there is no effect measure modification by the covariates. Robins introduced the “g-methods” to estimate such quantities using observational data [3]. Among these, the marginal structural models (MSMs) were designed to estimate marginal quantities (i.e., not conditional on other covariates). The parameters of a MSM can be consistently estimated using two classes of estimators: the g- (...truncated)


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Aolin Wang, Onyebuchi A. Arah, Roch A. Nianogo. G-computation of average treatment effects on the treated and the untreated, BMC Medical Research Methodology, 2017,