Events

The increasing availability of clinical measures, such as electronic medical records, has enabled the collection of diverse information including multiple longitudinal measurements and survival outcomes. Consequently, there is a need to utilize methods that can examine the associations between exposures and longitudinal measurement outcomes simultaneously. This statistical approach is known as joint modeling of longitudinal and survival data, which typically involves integrating linear mixed effects models for longitudinal measurements with Cox models for censored survival outcomes.

This method is motivated by various clinical scenarios. For instance, patients with Cystic Fibrosis, a genetic lung disorder, face risks like exacerbation, lung transplantation, or mortality, and are regularly monitored with multiple biomarkers. Similarly, patients recovering from stroke undergo longitudinal assessments to track their progress over time. Although these outcomes are biologically interconnected, they are often analyzed separately in practice.

Analyzing such complex data presents several challenges. One key challenge is accurately characterizing patients’ longitudinal profiles that influence survival outcomes. It’s commonly assumed that the underlying longitudinal values are associated with survival outcomes, but sometimes specific aspects of these profiles, like the rate of biomarker progression, affect the hazard differently. Choosing the right functional form for this relationship is crucial and requires careful investigation due to its potential impact on results.

Another challenge arises from the high dimensionality of some datasets, such as registry data. Analyzing such comprehensive datasets using complex methodologies can be computationally expensive. Therefore, there’s a demand for algorithms capable of distributed analyses that can concurrently and impartially explore multiple correlated outcomes.

In this presentation, we will explore strategies to tackle these challenges effectively.

Multilevel functional principal components analysis (MFPCA) facilitates estimation of hierarchical covariance structures for functional data produced by wearable sensors, including continuous glucose monitors (CGM), all while accounting for covariate effects. There are several existing methods to efficiently fit these types of models, including the eminent fast covariance estimation and the recently proposed procedure of fitting appropriate localized mixed effects models and smoothing (Xiao et al. 2016, Leroux et al. 2023). However, these methods do not inherently account for uncertainty in the eigenfunctions during the estimation procedure. Most rely on bootstrap or asymptotic analytic results to perform inference after estimation. Towards this end, we fit MFPCA within a fully-Bayesian framework using MCMC, treating the orthogonal eigenfunctions as random. A model constructed in this way automatically accounts for variability in eigenfunction estimation and its interplay with both features of the data and the assumed hierarchical structure. The flexibility of this method also makes it well-suited to exploring the imposition of additional constraints on the eigenfunctions, such as mutual orthogonality across levels. We assess the convergence of our model using Grassmannian distances between the spaces spanned by sampled eigenfunctions at each level. After performing validation using a variety of simulated functional data, we compare the results of our model to the prominent existing approaches using 4-hour windows of CGM data for persons with diabetes centered around known mealtimes.

The analysis of environmental mixtures is of growing importance in environmental epidemiology, and one of the key goals in such analyses is to identify exposures and their interactions that are associated with adverse health outcomes. Typical approaches utilize flexible regression models combined with variable selection to identify important exposures and estimate a potentially nonlinear relationship with the outcome of interest. Despite this surge in interest, no approaches to date can identify exposures and interactions while controlling any form of error rates with respect to exposure selection. We propose two novel approaches to estimating the health effects of environmental mixtures that simultaneously 1) estimate and provide valid inference for the overall mixture effect, and 2) identify important exposures and interactions while controlling the false discovery rate. We show that this can lead to substantial power gains to detect weak effects of environmental exposures. We apply our approaches to a study of persistent organic pollutants and find that controlling the false discovery rate leads to substantially different conclusions.

Consortiums of health databases utilize standardized vocabularies to facilitate multi-institutional studies based upon their constituent data. However, synthesizing this heterogeneous clinical data is hampered by variation between ostensibly unified terminologies, with each constituent dataset providing a different set of clinical covariates. Notably, we observe ontological relationships among these covariates, and those related covariates likely contribute similarly to treatment decisions and health outcomes. Here, we extend the Bayesian hierarchical model framework by encoding ontological relations among covariates in the form of correlations in corresponding parameters. Additionally, to deal with the large number of covariates in the observational health databases, we introduce the skew-shrinkage technique. Such technique directs parameter estimates either toward the null value or informed based on the evidence supported by the data. We developed a modified ziggurat algorithm to address the computational challenges in updating the local-scale parameters under the skewed horseshoe priors. We demonstrate our approach in a transfer learning task, using a causal model trained on a larger database to improve the treatment effect estimate in a smaller database.