Bayesian Modeling of Latent Heterogeneity in Complex Survey Data and Electronic Health Records
By: Rebecca Anthopolos
Publisher: [publisher not identified]
Published: 2019
Place of publication: [New York, N.Y.?]
Language: eng
Description: In population health, the study of unobserved, or latent, heterogeneity in longitudinal data may help inform public health interventions. Growth mixture modeling is a flexible tool for modeling latent heterogeneity in longitudinal data. However, the application of growth mixture models to certain data types, namely, complex survey data and electronic health records, is underdeveloped. For valid statistical inferences in complex survey data, features of the sample design must be incorporated into statistical analysis. In electronic health records, the application of growth mixture modeling is challenged by high levels of missing values. In this dissertation, I have three goals: First, I propose a Bayesian growth mixture model for complex survey data in which I directly incorporate features of the complex sample design. Second, I extend a Bayesian growth mixture model of multiple longitudinal health outcomes collected in electronic health records to a shared parameter model that can account for dierent missing data assumptions.

Third, I develop open-source software packages in R for each method that can be used for model tting, selection, and checking.
Notes: Department: Biostatistics.

Thesis advisor: Ying Wei.

Thesis advisor: Qixuan Chen.

Thesis (Dr.P.H.)--Mailman School of Public Health, Columbia University, 2019.