Performance of variable and function selection methods for estimating the non-linear health effects of correlated chemical mixtures: a simulation study
Nina Lazarevic, Luke D. Knibbs, Peter D. Sly, Adrian G. Barnett
Statistical methods for identifying harmful chemicals in a correlated mixture often assume linearity in exposure-response relationships. Non-monotonic relationships are increasingly recognised (e.g., for endocrine-disrupting chemicals); however, the impact of non-monotonicity on exposure selection has not been evaluated. In a simulation study, we assessed the performance of Bayesian kernel machine regression (BKMR), Bayesian additive regression trees (BART), Bayesian structured additive regression with spike-slab priors (BSTARSS), and lasso penalised regression. We used data on exposure to 12 phthalates and phenols in pregnant women from the U.S. National Health and Nutrition Examination Survey to simulate realistic exposure data using a multivariate copula. We simulated datasets of size N = 250 and compared methods across 32 scenarios, varying by model size and sparsity, signal-to-noise ratio, correlation structure, and exposure-response relationship shapes. We compared methods in terms of their sensitivity, specificity, and estimation accuracy. In most scenarios, BKMR and BSTARSS achieved moderate to high specificity (0.56--0.91 and 0.57--0.96, respectively) and sensitivity (0.49--0.98 and 0.25--0.97, respectively). BART achieved high specificity ($\geq$ 0.96), but low to moderate sensitivity (0.13--0.66). Lasso was highly sensitive (0.75--0.99), except for symmetric inverse-U-shaped relationships ($\leq$ 0.2). Performance was affected by the signal-to-noise ratio, but not substantially by the correlation structure. Penalised regression methods that assume linearity, such as lasso, may not be suitable for studies of environmental chemicals hypothesised to have non-monotonic relationships with outcomes. Instead, BKMR and BSTARSS are attractive methods for flexibly estimating the shapes of exposure-response relationships and selecting among correlated exposures.
Exposures to environmental chemicals during gestation can alter health status later in life. Most studies of maternal exposure to chemicals during pregnancy have focused on a single chemical exposure observed at high temporal resolution. Recent research has turned to focus on exposure to mixtures of multiple chemicals, generally observed at a single time point. We consider statistical methods for analyzing data on chemical mixtures that are observed at a high temporal resolution. As motivation, we analyze the association between exposure to four ambient air pollutants observed weekly throughou…
The Ohio State University（俄亥俄州立大学）；Princeton University（普林斯顿大学）
We initiate the study of a new model of supervised learning under privacy constraints. Imagine a medical study where a dataset is sampled from a population of both healthy and unhealthy individuals. Suppose healthy individuals have no privacy concerns (in such case, we call their data "public") while the unhealthy individuals desire stringent privacy protection for their data. In this example, the population (data distribution) is a mixture of private (unhealthy) and public (healthy) sub-populations that could be very different. Inspired by the above example, we consider a model in which the p…
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Estimating marginal association between paired outcomes in clustered data with an application to analyzing caries and periodontal disease data from the National Health and Nutrition Examination Survey
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