BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates

TL;DR

BoXHED is a new software package that nonparametrically estimates hazard functions with dynamic covariates using gradient boosting, enabling detailed analysis of health risk over time from high-frequency monitoring data.

Contribution

It introduces the first public implementation of a nonparametric hazard estimator for time-dependent covariates based on gradient boosting.

Findings

Revealed novel interaction effects among risk factors for cardiovascular disease.

Demonstrated the effectiveness of BoXHED on real health data.

Potentially resolves open questions in clinical risk modeling.

Abstract

The proliferation of medical monitoring devices makes it possible to track health vitals at high frequency, enabling the development of dynamic health risk scores that change with the underlying readings. Survival analysis, in particular hazard estimation, is well-suited to analyzing this stream of data to predict disease onset as a function of the time-varying vitals. This paper introduces the software package BoXHED (pronounced 'box-head') for nonparametrically estimating hazard functions via gradient boosting. BoXHED 1.0 is a novel tree-based implementation of the generic estimator proposed in Lee, Chen, Ishwaran (2017), which was designed for handling time-dependent covariates in a fully nonparametric manner. BoXHED is also the first publicly available software implementation for Lee, Chen, Ishwaran (2017). Applying BoXHED to cardiovascular disease onset data from the Framingham Heart Study reveals novel interaction effects among known risk factors, potentially resolving an open question in clinical literature.