Debajyoti Sinha
Florida State University
Analysis of spatially clustered survival data with unobserved
covariates using SBART
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 under the Bayesian ensemble learning paradigm called Soft Bayesian Additive Regression Trees (SBART in short).
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 computational tool based on latent variables to address our computational challenges.
We illustrate the practical implementation of our method and its advantages over existing methods 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. Florida Cancer Registry (FCR) is used to obtain clustered survival data with patient-level covariates, and the Behavioral Risk Factor Surveillance Survey (BRFSS) is used as to obtain further data information on an unobservable county-level covariate of Screening Mammography Utilization (SMU). We also compare our method with existing analysis methods through simulation studies.
DATE: Wednesday, September 4, 2024, 4:00 PM, AUST 202
Webex link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m6b547a9da5197fe478e602ea538c4833
Coffee will be served at 3:30 in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu