AIMC Topic: Data Interpretation, Statistical

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High-dimensional multiple imputation for partially observed confounders including natural language processing-derived auxiliary covariates.

American journal of epidemiology
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 ...

Pulling back the curtain: the road from statistical estimand to machine-learning-based estimator for epidemiologists (no wizard required).

American journal of epidemiology
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...

A meta-learning method for estimation of causal excursion effects to assess time-varying moderation.

Biometrics
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...

Development of an Interpretable Machine Learning Model for Neurotoxicity Prediction of Environmentally Related Compounds.

Environmental science & technology
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...

Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial With Causal Forests.

International journal of methods in psychiatric research
BACKGROUND: Flexible machine learning tools are increasingly used to estimate heterogeneous treatment effects.

Assessing the accuracy and efficiency of Chat GPT-4 Omni (GPT-4o) in biomedical statistics: Comparative study with traditional tools.

Saudi medical journal
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...

AIPW: An R Package for Augmented Inverse Probability-Weighted Estimation of Average Causal Effects.

American journal of epidemiology
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...

Thirteen Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!).

American journal of epidemiology
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...