Efficient Estimation of Optimal Regimes Under a No Direct Effect Assumption
成果类型：
Article
署名作者：
Liu, Lin; Shahn, Zach; Robins, James M.; Rotnitzky, Andrea
署名单位：
Shanghai Jiao Tong University; Shanghai Jiao Tong University; International Business Machines (IBM); IBM USA; Harvard University; Universidad Torcuato Di Tella; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2020.1856117
发表日期：
2021
页码：
224-239
关键词：
摘要：
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). for this article are available online.