Multiple imputation (MI) models can be improved with auxiliary covariates (ACs), but their performance in high-dimensional data remains unclear. We aimed to develop and compare high-dimensional MI (HDMI) methods using structured and natural language ...
Epidemiologists increasingly use causal inference methods that rely on machine learning, as these approaches can relax unnecessary model specification assumptions. While deriving and studying asymptotic properties of such estimators is a task usually...
Advances in wearable technologies and health interventions delivered by smartphones have greatly increased the accessibility of mobile health (mHealth) interventions. Micro-randomized trials (MRTs) are designed to assess the effectiveness of the mHea...
The rising prevalence of nervous system disorders has become a significant global health challenge, with environmental pollutants identified as key contributors. However, the large number of environmental related compounds, combined with the low effi...
OBJECTIVES: To assess the accuracy of ChatGPT-4 Omni (GPT-4o) in biomedical statistics. The recent novel inauguration of Artificial Intelligence ChatGPT-Omni (GPT-4o), has emerged with the potential to analyze sophisticated and extensive data sets, c...
Artificial intelligence (AI) models often face performance drops after deployment to external datasets. This study evaluated the potential of a novel data augmentation framework based on generative adversarial networks (GANs) that creates synthetic p...
An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estima...
Machine learning is gaining prominence in the health sciences, where much of its use has focused on data-driven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspeci...
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