AI-assisted exposure-response data analysis: Quantifying heterogeneous causal effects of exposures on survival times.

AI-assisted data analysis can help risk analysts better understand exposure-response relationships by making it relatively easy to apply advanced statistical and machine learning methods, check their assumptions, and interpret their results. This paper demonstrates the potential of large language models (LLMs), such as ChatGPT, to facilitate statistical analyses, including survival data analyses, for health risk assessments. Through AI-guided analyses using relatively recent and advanced methods such as Individual Conditional Expectation (ICE) plots using Random Survival Forests and Heterogeneous Treatment Effects (HTEs) estimated using Causal Survival Forests, population-level exposure-response functions can be disaggregated into individual-level exposure-response functions.

MicroRNA signatures of VO peak in older adult participants of the Study of Muscle, Mobility and Aging.

Background: Peak oxygen consumption during exercise (VO peak), is a direct measure of cardiorespiratory fitness (CF), a key indicator of physical function and overall health. However, the molecular changes that underpin VO peak variation are not clear. Our objective is to understand the miRNA signatures that relate to VO peak variation, which could provide insights to novel mechanisms that contribute to low VO peak.

The use of public spatial databases in risk analysis: A US-oriented tutorial.

This tutorial focuses on opportunities and challenges associated with using six large, publicly accessible spatial databases published during the last decade by US federal agencies. These databases provide opportunities for researchers to risk-inform policy by comparing community asset, demographic, economic, and social data, along with anthropogenic and natural hazard data at multiple geographic scales. The opportunities for data analysis come with challenges, including data accuracy, variations in the shape and size of data cells, spatial autocorrelation, and other issues endemic to spatial datasets.

Clarifying causality and information flows between time series: Particulate air pollution, temperature, and elderly mortality.

Exposure-response associations between fine particulate matter (PM2.5) and mortality have been extensively studied but potential confounding by daily minimum and maximum temperatures in the weeks preceding death has not been carefully investigated. This paper seeks to close that gap by using lagged partial dependence plots (PDPs), sorted by importance, to quantify how mortality risk depends on lagged values of PM2.