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
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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
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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)