On Soft Bayesian Additive Regression Trees and asynchronous longitudinal regression analysis

TL;DR

This paper introduces a weighted SBART model tailored for asynchronous longitudinal data, demonstrating its effectiveness through simulations and an HIV study application, advancing non-parametric Bayesian regression methods.

Contribution

It develops a novel weighted SBART approach specifically designed for irregular, asynchronous longitudinal data, with theoretical and practical advantages over existing methods.

Findings

The proposed method performs well in extensive simulations.

It effectively handles irregular and mismatched observation times.

Application to HIV data illustrates practical utility.

Abstract

In many longitudinal studies, the covariate and response are often intermittently observed at irregular, mismatched and subject-specific times. How to deal with such data when covariate and response are observed asynchronously is an often raised problem. Bayesian Additive Regression Trees(BART) is a Bayesian non-Parametric approach which has been shown to be competitive with the best modern predictive methods such as random forest and boosted decision trees. The sum of trees structure combined with a Bayesian inferential framework provide a accurate and robust statistic method. BART variant soft Bayesian Additive Regression Trees(SBART) constructed using randomized decision trees was developed and substantial theoretical and practical benefits were shown. In this paper, we propose a weighted SBART model solution for asynchronous longitudinal data. In comparison to other methods, the current methods are valid under with little assumptions on the covariate process. Extensive simulation studies provide numerical support for this solution. And data from an HIV study is used to illustrate our methodology