Efficient Estimation of Optimal Regimes Under a No Direct Effect Assumption

原文摘要

Lin Liua , Zach Shahnb, James M. Robinsc* & Andrea Rotnitzkyda Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC and SJTU-Yale Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China; b IBM Research, Yorktown Heights, NY; c Department of Biostatistics and Epidemiology, Harvard University, Boston, MA; d Department of Economics, Universidad Torcuato Di Tella and CONICET, Buenos Aires, ArgentinaSupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT James M. Robins robins@hsph.harvard.edu Department of Biostatistics and Epidemiology, Harvard University, Boston, MA 02115.AbstractWe 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.

摘要来源：OpenAlex