Efficient Estimation of Optimal Regimes Under a No Direct Effect Assumption

Lin Liu, Zach Shahn, James M. Robins and Andrea Rotnitzky

Journal of the American Statistical Association, 2021, vol. 116, issue 533, 224-239

Abstract: We derive new estimators of an optimal joint testing and treatment regime under the no direct effect (NDE) assumption that a given laboratory, diagnostic, or screening test has no effect on a patient’s clinical outcomes except through the effect of the test results on the choice of treatment. We model the optimal joint strategy with an optimal structural nested mean model (opt-SNMM). The proposed estimators are more efficient than previous estimators of the parameters of an opt-SNMM because they efficiently leverage the “NDE of testing” assumption. Our methods will be of importance to decision scientists who either perform cost-benefit analyses or are tasked with the estimation of the “value of information” supplied by an expensive diagnostic test (such as an MRI to screen for lung cancer). Supplementary materials for this article are available online.

Date: 2021
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DOI: 10.1080/01621459.2020.1856117

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