| Title |
G-computation of average treatment effects on the treated and the untreated
|
|---|---|
| Published in |
BMC Medical Research Methodology, January 2017
|
| DOI | 10.1186/s12874-016-0282-4 |
| Pubmed ID | |
| Authors |
Aolin Wang, Roch A. Nianogo, Onyebuchi A. Arah |
| Abstract |
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. 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. 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). 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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | <1% |
| Unknown | 103 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 20% |
| Student > Ph. D. Student | 16 | 15% |
| Student > Master | 11 | 11% |
| Student > Doctoral Student | 8 | 8% |
| Professor > Associate Professor | 6 | 6% |
| Other | 12 | 12% |
| Unknown | 30 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 21 | 20% |
| Economics, Econometrics and Finance | 7 | 7% |
| Social Sciences | 7 | 7% |
| Mathematics | 6 | 6% |
| Agricultural and Biological Sciences | 5 | 5% |
| Other | 19 | 18% |
| Unknown | 39 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 March 2022.
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#13,479,905
of 23,726,221 outputs
Outputs from BMC Medical Research Methodology
#1,240
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Outputs of similar age
#205,368
of 424,934 outputs
Outputs of similar age from BMC Medical Research Methodology
#17
of 31 outputs
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