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

• Published in: BMC medical research methodology Vol. 17; no. 1; pp. 3 - 5
• Main Authors: Wang, Aolin, Nianogo, Roch A., Arah, Onyebuchi A.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 09.01.2017; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Analysis; Angina Pectoris - diagnosis; Angina Pectoris - epidemiology; Angina Pectoris - therapy; Average treatment effects on the treated (ATT); Average treatment effects on the untreated (ATU); Computer Simulation; Data analysis statistics and modelling; Educational Status; Epidemiology; G-computation; Health Sciences; Humans; Medical research; Medicine; Medicine & Public Health; Models, Statistical; Monte Carlo Method; Monte Carlo methods; Parametric g-formula; Resampling; Research Article; Simulation; Statistical Theory and Methods; Statistics for Life Sciences; Theory of Medicine/Bioethics; Treatment Outcome; G-computation; Parametric g-formula; Simulation; Average treatment effects on the treated (ATT); Average treatment effects on the untreated (ATU); Resampling
• ISSN: 1471-2288; 1471-2288
• 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.