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arXiv · 1908.10448

Efficient estimation of optimal regimes under a no direct effect assumption

Lin Liu·Zach Shahn·James M. Robins·Andrea Rotnitzky
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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 using an optimal regime 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 `no direct effect (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).

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Lin Liu, Zach Shahn, James M. Robins, Andrea Rotnitzky. 2019-08-27. Efficient estimation of optimal regimes under a no direct effect assumption. https://arxiv.org/abs/1908.10448

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