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

On Soft Bayesian Additive Regression Trees and asynchronous longitudinal regression analysis