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Estimation and false discovery control for the analysis of environmental mixtures

Wednesday, November 15, 2023

Speaker: Dr. Srijata Samanta (Bristol Myers Squibb)

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.

The Modified Ziggurat Algorithm for Skewed Shrinkage Prior

Wednesday, October 18, 2023

Speaker: Yihao Gu (Fudan University)

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.

BLAST trainee presentations

Tuesday, October 3, 2023

Speaker: Andrew Chin, Yuzheng Dun, Claire Heffernan, Sandipan Pramanik (Johns Hopkins University)

A series of talks given by current PhD students and postdocs in the BLAST group.

Spatial predictions on physically constrained domains: Applications to Arctic sea salinity data

Wednesday, September 27, 2023

Speaker: Dr. Bora Jin (Johns Hopkins University)

In this paper, we predict sea surface salinity (SSS) in the Arctic Ocean based on satellite measurements. SSS is a crucial indicator for ongoing changes in the Arctic Ocean and can offer important insights about climate change. We particularly focus on areas of water mistakenly flagged as ice by satellite algorithms. To remove bias in the retrieval of salinity near sea ice, the algorithms use conservative ice masks, which result in considerable loss of data. We aim to produce realistic SSS values for such regions to obtain more complete understanding about the SSS surface over the Arctic Ocean and benefit future applications that may require SSS measurements near edges of sea ice or coasts. We propose a class of scalable nonstationary processes that can handle large data from satellite products and complex geometries of the Arctic Ocean. Barrier Overlap-Removal Acyclic directed graph GP (BORA-GP) constructs sparse directed acyclic graphs (DAGs) with neighbors conforming to barriers and boundaries, enabling characterization of dependence in constrained domains. The BORA-GP models produce more sensible SSS values in regions without satellite measurements and show improved performance in various constrained domains in simulation studies compared to state-of-the-art alternatives. An R package is available on GitHub.