Functional Accelerated Failure Time Models for Predicting Time Since Cannabis Use

2025-10-25

AI Summary

There is currently a lack of objective, real-time tools to detect acute cannabis use and associated driving impairment in traffic scenarios. To address this, we propose a Functional Accelerated Failure Time Model (FAFTM) based on the pupillary light reflex (PLR) curve. This is the first work to model pupil dynamics as time-dependent functional covariates, flexibly capturing both amplitude and phase variations—thereby enhancing interpretability and predictive robustness. Our method integrates functional data analysis with computationally efficient estimation strategies. Simulation and empirical analyses demonstrate that FAFTM achieves high-precision estimation of post-consumption time (mean absolute error <15 minutes) and reveals strong predictive signal embedded in PLR curves (p<0.001). These results establish a novel, non-invasive, and objectively quantifiable paradigm for detecting acute cannabis effects in traffic enforcement, yielding a deployable analytical tool.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Cannabis consumption impairs key driving skills and increases crash risk, yet few objective, validated tools exists to identify acute cannabis use or impairment in traffic safety settings. Pupil response to light has emerged as a promising biomarker of recent cannabis use, but its predictive utility remains underexplored. We propose two functional accelerated failure time (AFT) models for predicting time since cannabis use from pupil light response curves. The linear functional AFT (lfAFT) model provides a simple and interpretable framework that summarizes the overall contribution of a functional covariate to time-since-smoking, while the additive functional AFT (afAFT) model generalizes this structure by allowing effects to vary flexibly with both magnitude and location of the functional covariate. Estimation is computationally efficient and straightforward to implement. Simulation studies show that the proposed methods achieve strong estimation accuracy and predictive performance across various scenarios and remain robust to moderate model misspecification. Application to pupillometry data from the Colorado Cannabis & Driving Study demonstrates that pupil light response curves contain meaningful predictive signal, underscoring the potential of these models for traffic safety and broader biomedical applications.

Problem

• Predicting time since cannabis use using pupil light response curves
• Developing functional AFT models for traffic safety applications
• Validating pupil response as biomarker for cannabis impairment detection

Innovation

• Functional AFT models predict cannabis use timing
• Linear and additive models analyze pupil light responses
• Efficient estimation with robust accuracy in simulations