Deep Learning-based Emulation Analysis: Methodological Developments and Case Studies

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Project Summary To objectively quantify the relative effectiveness of drugs, devices, and treatment procedures on survival outcomes of cardiovascular diseases (CVDs), rigorously designed and executed randomized clinical trials (RCTs) remain as the gold standard. However, for many problems, RCTs either have failed or are not feasible. Luckily, the fast development of electronic medical record (EMR) and insurance claims databases makes it possible to mine a large amount of observational data and efficiently complement RCTs. Among the available observational data analysis techniques that aim to draw RCT-type conclusions, emulation has emerged as especially attractive, given its trial-like architecture, interpretability, and scalability. It has been applied to CVDs for over twenty years and led to many important findings. This study has two aims. The first aim is to develop a deep learning (DL)-based emulation analysis pipeline, methods, and software. Most of the existing emulation analyses are based on “classic” regression techniques. Very recently, our group was the first to develop DL-based emulation analysis with application to CVDs. Compared to regression, DL excels by having superior model fitting and flexibly accommodating unspecified nonlinear effects. Built on our recent success, this project will methodologically significantly advance by developing cutting-edge DL-based emulation analysis with more effective estimation (that has the much- desired robustness property and significantly improved stability and interpretability), comprehensive and valid inference (which is essential for making definitive conclusions on treatment effects but missing in most DL studies), and friendly software (to facilitate broad utilization). This methodological effort can substantially expand the scope of emulation analysis, deep learning, causal inference, observational data analysis, and medical record/insurance claims data analysis. The second aim is to conduct two clinically highly significant case studies. The first case study is on evaluating the effect of ICD (Implantable Cardioverter Defibrillator) on all-cause mortality in the VA (Department of Veterans Affairs) elderly population. The clinical trial targeting at addressing this problem failed because of low enrollment. As part of the VA CAUSAL Initiative, emulation was proposed as a viable solution to “replace” the trial. The second case study is on evaluating the comparative efficacy of Rivaroxaban versus Dabigatran on the mortality of AF (atrial fibrillation) patients in the Medicare population, for which an RCT is unlikely with both drugs FDA-approved and already popularly used. Beyond directly informing clinical practice, research under this aim can also complement and advance the VA CAUSAL Initiative as well as serve as a prototype for future applications of the proposed approach.