An application programming interface implementing Bayesian approaches for evaluating effect of time-varying treatment with R and Python

Abstract

Introduction: Methods and tools evaluating treatment effect have been primarily developed for binary type of treatment. Yet, treatment is rarely binary outside the experimental setting, varies by dosage, frequency and time. Treatment is routinely adjusted, initiated or stopped when being administered over a period of time. Methods: Both Gaussian Process (GP) regression and Bayesian additive regression tree (BART) have been used successfully for handling complex setting involving time-varying treatments that is either adaptive or non-adaptive. Here, we introduce an application programming interface (API) that implements both BART and GP for estimating averaged treatment effect (ATE) and conditional averaged treatment (CATE) for the two-stage time-varying treatment strategies. Results: We provide two real applications for evaluating comparative effectiveness of time-varying treatment strategies. The first example evaluates an early aggressive treatment strategies for caring children with newly diagnosed Juvenile Idiopathic Arthritis (JIA). The second evaluates the persistent per-protocol treatment effectiveness in a large randomized pragmatic trial. The examples demonstrate the use of the API calling from R and Python, for handling both non-adaptive or adaptive treatments, with presences of partially observed or missing data issues. Summary tables and interactive figures of the results are downloadable.