From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects
Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive the assumptions that are required to identify population average tr...
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Published in | Journal of the Royal Statistical Society. Series A, Statistics in society Vol. 178; no. 3; pp. 757 - 778 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Oxford
Blackwell Publishing Ltd
01.06.2015
John Wiley & Sons Ltd Oxford University Press |
Subjects | |
Online Access | Get full text |
ISSN | 0964-1998 1467-985X |
DOI | 10.1111/rssa.12094 |
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Abstract | Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive the assumptions that are required to identify population average treatment effects from RCTs. We provide placebo tests, which formally follow from the identifying assumptions and can assess whether they hold. We offer new research designs for estimating population effects that use non-randomized studies to adjust the RCT data. This approach is considered in a cost-effectiveness analysis of a clinical intervention: pulmonary artery catheterization. |
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AbstractList | Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive the assumptions that are required to identify population average treatment effects from RCTs. We provide placebo tests, which formally follow from the identifying assumptions and can assess whether they hold. We offer new research designs for estimating population effects that use non-randomized studies to adjust the RCT data. This approach is considered in a cost-effectiveness analysis of a clinical intervention: pulmonary artery catheterization. Reprinted by permission of Blackwell Publishers Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive the assumptions that are required to identify population average treatment effects from RCTs. We provide placebo tests, which formally follow from the identifying assumptions and can assess whether they hold. We offer new research designs for estimating population effects that use non-randomized studies to adjust the RCT data. This approach is considered in a cost-effectiveness analysis of a clinical intervention: pulmonary artery catheterization. Summary Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive the assumptions that are required to identify population average treatment effects from RCTs. We provide placebo tests, which formally follow from the identifying assumptions and can assess whether they hold. We offer new research designs for estimating population effects that use non‐randomized studies to adjust the RCT data. This approach is considered in a cost‐effectiveness analysis of a clinical intervention: pulmonary artery catheterization. |
Author | Hartman, Erin Grieve, Richard Sekhon, Jasjeet S. Ramsahai, Roland |
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SubjectTerms | Average Averages Catheterization Causal inference Clinical trials Cost analysis Cost effectiveness analysis Cost efficiency Cost-effectiveness Cost-effectiveness studies Entropy Estimation Estimation bias External validity Intubation Medical treatment Observational studies Placebo tests Placebos Political action committees Population Population distributions Population estimates Pulmonary arteries Pulmonary artery catheterization Randomized controlled trials Sample surveys Side effects Statistics |
Title | From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects |
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