Novel and Rigorous Statistical Learning and Inference for Comparative Effectiveness Research with Complex Data

Project Summary Comparative effectiveness research (CER) in medicine is commonly conducted to discover and provide infor- mation on possible differences between alternative drugs or treatments in their effectiveness and safety. Such information, if reliable and accurate, can help patients, clinicians, and other healthcare stakeholders to make better-informed healthcare decisions and improve healthcare delivery and outcomes. However, drawing valid and relevant inferences about treatment effects from observational studies involves effort and expertise from both subject-matter researchers and statisticians. On one hand, causal inference relies on structural assump- tions. Two prominent classes of such assumptions are unconfoundedness or instrument variable (IV) assump- tions. On the other hand, granted the structural assumptions, causal inference also requires statistical modeling and estimation of population properties and associations from empirical data. The problem of statistical learning and inference can be challenging, while allowing a large number of candidate regressors such as main effects and interactions of covariates. The objective of our research is to develop, evaluate, and disseminate a new set of theoretically rigorous, numerically automated, and practically useful methods of statistical learning and inference for estimating treatment effects in CER with complex, high-dimensional data. Three specific aims are (1) high-dimensional inference about population and subpopulation average treatment effects under uncon- foundedness with multi-valued treatments, (2) high-dimensional inference about local average treatment effects and IV-dependent average treatment effects on the treated with multi-valued instruments and treatments, and (3) high-dimensional inference about average treatment effects such as contrasts between survival and hazard probabilities with longitudinal and survival data. We will investigate applications of the new methods to several comparative effectiveness and safety studies including a recent study on comparative treatment strategies in schizophrenia and an ongoing project to evaluate the therapeutic exchangeability of same-class drugs, for ex- ample, direct oral anticoagulants among patients with atrial fibrillation or atrial flutter or dipeptidyl peptidase-4 inhibitors among patients with type 2 diabetes, while exploiting IVs created by the design of the Medicare pre- scription drug benefit. We will develop and publicly release user-friendly computer software including transparent documentation for direct implementation of the new methods.