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    Regression-adjusted matching and double-robust methods for estimating average treatment effects

    Kreif, N. and Grieve, R. and Radice, Rosalba and Sekhon, J.S. (2013) Regression-adjusted matching and double-robust methods for estimating average treatment effects. Health Services and Outcomes Research Methodology 13 (2-4), pp. 174-202. ISSN 1387-3741.

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    Regression, propensity score (PS) and double-robust (DR) methods can reduce selection bias when estimating average treatment effects (ATEs). Economic evaluations of health care interventions exemplify complex data structures, in that the covariate–endpoint relationships tend to be highly non-linear, with highly skewed cost and health outcome endpoints. When either the regression or PS model is correct, DR methods can provide unbiased, efficient estimates of ATEs, but generally the specification of both models is unknown. Regression-adjusted matching can also protect against bias from model misspecification, but has not been compared to DR methods. This paper compares regression-adjusted matching to selected DR methods (weighted regression and augmented inverse probability of treatment weighting) as well as to regression and PS methods for addressing selection bias in cost-effectiveness analyses (CEA). We contrast the methods in a CEA of a pharmaceutical intervention, where there are extreme estimated PSs, hence unstable inverse probability of treatment (IPT) weights. The case study motivates a simulation which considers settings with functional form misspecification in the PS and endpoint regression models (e.g. cost model with log instead of identity link), stable and unstable PS weights. We find that in the realistic setting of unstable IPT weights and misspecifications to the PS and regression models, regression-adjusted matching reports less bias than DR methods. We conclude that regression-adjusted matching is a relatively robust method for estimating ATEs in applications with complex data structures exemplified by CEA.


    Item Type: Article
    Keyword(s) / Subject(s): average treatment effect, inverse probability of treatment weighting, double-robustness, regression-adjusted matching Cost-effectiveness analyses
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Depositing User: Sarah Hall
    Date Deposited: 15 Apr 2014 13:57
    Last Modified: 02 Aug 2023 17:10


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