Bayesian Latent Class Model for Predicting Gestational Age in Health Administrative Data

• Activity Number: 290 - Advanced Bayesian Topics (Part 3)
• Type: Contributed
• Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Shuang Wang* and Gavino Puggioni and Xuerong Wen
• Companies: University of Rhode Island and University of Rhode Island and University of Rhode Island
• Keywords: Bayesian latent class model; Finite mixture model; Gestational age at birth; Administrative Data

Abstract

Health administrative data are oftentimes of limited use in obstetric research due to lacking data on gestational age at birth (GAB). Algorithms estimating GAB can introduce bias in estimation without accounting for the unique distributional shape of GAB. Hence, we develop a Bayesian Latent class model to predict GAB. The model employs a mixture of Gaussian distributions with linear covariates within each class. This approach allows modeling heterogeneity in population by identifying latent subgroups and estimating class-specific regression coefficients. We fit this model in a Bayesian framework conducting posterior computation with Markov Chain Monte Carlo methods. The method is applied to a dataset of Rhode Island Medicaid mother-child pairs. The results indicate Medicaid women were partitioned into three latent classes, featured by extreme preterm birth, preterm or ''early'' term birth, and ''late'' term birth. Obstetrical complications appeared to pose a significant influence on class allocation. Compared to traditional linear models our approach shows an advantageous predictive accuracy and superior flexibility in modeling a skewed response and population heterogeneity.