Assessing variable importance in survival analysis using machine learning

• 成果类型: Article
• 署名作者: Wolock, C. J.; Gilbert, P. B.; Simon, N.; Carone, M.
• 署名单位: University of Pennsylvania; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asae061
• 发表日期: 2025
• 关键词: predictive accuracy; explained variation; hiv; models; acquisition; men; sex

摘要

Given a collection of features available for inclusion in a predictive model, it may be of interest to quantify the relative importance of a subset of features for the prediction task at hand. For example, in HIV vaccine trials, participant baseline characteristics are used to predict the probability of HIV acquisition over the intended follow-up period, and investigators may wish to understand how much certain types of predictors, such as behavioural factors, contribute to overall predictiveness. Time-to-event outcomes such as time to HIV acquisition are often subject to right censoring, and existing methods for assessing variable importance are typically not intended to be used in this setting. We describe a broad class of algorithm-agnostic variable importance measures for prediction in the context of survival data. We propose a nonparametric efficient estimation procedure that incorporates flexible learning of nuisance parameters, yields asymptotically valid inference and enjoys double robustness. We assess the performance of our proposed procedure via numerical simulations and analyse data from the HVTN 702 vaccine trial to inform enrolment strategies for future HIV vaccine trials.