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).

翻译：在不产生直接效果的假设下,我们得出最佳联合测试和治疗制度的新估计数据,认为特定实验室、诊断或筛查测试对病人的临床结果没有影响,除非通过测试结果对治疗选择的影响。我们用最佳制度结构嵌套平均模型(opt-SNMM)来模拟最佳联合战略。拟议的估计数据比以前对选择-SNMM参数估计数据的效率更高,因为它们有效地利用了测试的“没有直接影响”的假设。我们的方法对于决定进行成本效益分析或负责估计昂贵诊断试验(如肺癌检查的MRI)所提供的“信息价值”的科学家非常重要。