In this talk I describe new, highly efficient estimators of optimal joint testing and treatment regimes under the no direct effect 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. The proposed estimators attain high efficiency because they leverage this "no direct effect of testing" (abbreviated as NDE) assumption.
What is surprising and, indeed, unprecedented in my experience, is that, in a substantive study of HIV infected subjects, our new estimators delivered a 50-fold increase in efficiency (and, thus, a 50 fold reduction in required sample size) compared to estimators that fail to leverage the NDE assumption! In this talk I review the results of this HIV study, describe the new estimators, and provide guidance as to when such large gains in efficiency are to be expected.
Areas in which our new, more efficient estimators should be particularly important is that of cost-benefit analyses wherein the costs of diagnostic tests (such as MRIs to screen for lung cancer, mammograms to screen for breast cancer, and urinary cytology to screen for bladder cancer) are weighed against the clinical value of the information supplied by the test results. In a political science context, candidates often conduct private polls and focus groups to help update campaign outreach decisions such as the number and content of social media and television ad buys to be allocated to various demographic groups. One can view private polls and focus groups as tests and outreach decisions as treatments that together satisfy no direct effect of testing on the outcome of the election except through the test results on the choice of treatment.
This is joint work with Lin Liu, Zach Shahn, and Andrea Rotnitzky