Observational data with a continuous exposure: Study design and data analysis
Statisticians and empirical researchers encounter observational data with a continuous exposure, either a treatment exposure or an IV-defined exposure, on a daily basis. Simple, ad-hoc methods that dichotomize the continuous exposure typically render the potential outcomes ill-defined, and obliterate the rich information contained in the original, continuous exposure. The current state-of-the-art study design approach to observational data with a continuous exposure is a technique called nonbipartite pair match; the method has enjoyed much success in empirical studies but suffers from two major limitations. From a theoretical perspective, a pair match is not optimal among the class of all subclassifications. From a very practical perspective, the pair match design often discards certain study units to design two groups well-balanced on observed covariates while separate in the exposure dose. In this talk, we propose a novel nonbipartite full match design that successfully solves both limitations. There are two types of data analysis facilitated by the proposed study design: instrumental variable (IV) analysis and dose-response relationship analysis. We illustrate the IV analysis using our recent empirical work investigating the association between intraoperative TEE use in CABG surgery and 30-day mortality rate, and the dose-response relationship analysis using our recent work on the effect of social mobility during the first phased reopening last year on subsequent COVID-19-related public health outcomes.