A1204
Title: Causal estimation of cluster-specific effects on spatially associated survival data using SoftBART
Authors: Debajyoti Sinha - Florida State University (United States) [presenting]
Abstract: The aim is to propose a novel Bayesian approach to estimate causal effects in spatially clustered survival data. Using soft Bayesian additive regression trees (SBART), a nonparametric regression is introduced for a log-normal survival model that accommodates spatial associations among unknown cluster effects through a directed acyclic graph autoregressive (DAGAR) model. A two-stage approach is employed, which entails estimating the propensity score in the first step and incorporating it as a confounder in the outcome model in the second step. The simulation study compares the method with existing approaches under various simulation scenarios, including correctly specified as well as misspecified outcome models, to demonstrate the satisfactory performance of the method. The method is applied to analyze the causal effect of treatment delay (TD) on post-treatment survival of breast cancer patients from the Florida Cancer Registry (FCR). The analysis produces the county-specific as well as state-wide assessment of the causal effects while accommodating spatial association among counties.
