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2024 Colloquia

Linking Longitudinal Methods toward Clinical Decision Support

Patrick Heagerty, Ph.D.
University of Washington

Visit the Charles L. Odoroff Memorial Lecture for details

Thursday, May 2, 2024
3:30 pm - 5:00 pm

Helen Wood Hall, Room 1W-501

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Estimating Interactions in Chemical Mixtures: Case Studies from Cancer Epidemiology

Paul S. Albert, Ph.D.
National Institutes of Health (NIH)

Estimating the interactions between mixture components is often of interest in epidemiologic studies. Motivated by studies examining the effects of chemical exposures in the development of cancer, we develop and compare different approaches for estimating interactions among components of these exposures. We develop a latent functions approach where the main and interaction effects are estimated using two separate sets of unobserved functions. We also develop a Bayesian shrinkage approach that incorporates the hierarchical principle which assumes that it is unlikely that there are interactions without the presence of corresponding main effects. These approaches are compared with Bayesian kernel machine regression (BKMR) and LASSO, two approaches currently being used for analyzing interactions in chemical mixture studies. We analyze the data from a series of NCI studies to provide insight into chemical mixture analyses.

This is joint work with Sung Duk Kim and Debamita Kundu.

Thursday, April 18, 2024

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Sparse Causal Learning

Linbo Wang, Ph.D.
University of Toronto

In many observational studies, researchers are interested in studying the effects of multiple exposures on the same outcome. Unmeasured confounding is a key challenge in these studies as it may bias the causal effect estimate. To mitigate the confounding bias, we introduce a novel device, called the synthetic instrument, to leverage the information contained in multiple exposures for causal effect identification and estimation. We show that under linear structural equation models, the problem of causal effect estimation can be formulated as an $\ell_0$-penalization problem, and hence can be solved efficiently using off-the-shelf software. Simulations show that our approach outperforms state-of-art methods in both low-dimensional and high-dimensional settings. We further illustrate our method using a mouse obesity dataset.

Thursday, March 21, 2024

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