Analysis of spatially clustered survival data with unobserved covariates using SBART

Music City Center 

Usual parametric and semi-parametric regression methods are inappropriate and inadequate for large clustered survival studies when the appropriate functional forms of the covariates and their interactions in hazard functions are unknown, and random cluster effects as well as some unknown cluster-level covariates are spatially correlated. We present a general nonparametric method for such studies using Soft Bayesian Additive Regression Trees (SBART). Our additional methodological and computational challenges include large number of clusters, variable cluster sizes, and proper statistical augmentation of the unobservable cluster-level covariate using a data registry different from the main survival study. We use an innovative 3-step tool based on latent variables to address our computational challenges.

We illustrate the practical implementation of our method by assessing the impacts of intervention in some cluster/county level and patient-level covariates to mitigate existing racial disparity in breast cancer survival in 67 Florida counties (clusters) using two different data resources.

We also compare our method with existing analysis methods through simulation studies.

Spatial survival analysis; Multiple data sources integration; Soft Bayesian Additive Regression Trees (SBART); Racial disparities; Life-Years Saved.