Functional Accelerated Failure Time Models for Predicting Time Since Cannabis Use

TL;DR

This paper introduces two novel functional accelerated failure time models that utilize pupil light response curves to accurately predict the time since cannabis use, aiding traffic safety assessments.

Contribution

The paper develops and validates two new functional AFT models that improve prediction of recent cannabis use from pupil response data, with efficient estimation and robustness.

Findings

Models achieve high estimation accuracy and predictive performance.

Pupil light response curves contain meaningful signals for recent cannabis use.

Models are robust to moderate model misspecification.

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.