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BMC Med Res Methodol. 2017 Jan 9;17(1):3. doi: 10.1186/s12874-016-0282-4.

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

Wang A1,2, Nianogo RA3,4, Arah OA3,4,5.

Author information

1
Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, CA, USA. aolinw@ucla.edu.
2
California Center for Population Research (CCPR), Los Angeles, CA, USA. aolinw@ucla.edu.
3
Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
4
California Center for Population Research (CCPR), Los Angeles, CA, USA.
5
UCLA Center for Health Policy Research, Los Angeles, CA, USA.

Abstract

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.

KEYWORDS:

Average treatment effects on the treated (ATT); Average treatment effects on the untreated (ATU); G-computation; Parametric g-formula; Resampling; Simulation

PMID:
28068905
PMCID:
PMC5223318
DOI:
10.1186/s12874-016-0282-4
[Indexed for MEDLINE]
Free PMC Article

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