Related Experiment Video
Updated: Sep 13, 2025

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
Published on: January 19, 2019
BoXHED2.0: Scalable Boosting of Dynamic Survival Analysis
Arash Pakbin1, Xiaochen Wang2, Bobak J Mortazavi1
1Texas A&M University.
None:
Modern applications of survival analysis increasingly involve time-dependent covariates. The Python package BoXHED2.0 (Boosted eXact Hazard Estimator with Dynamic covariates) is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from www.github.com/BoXHED.
Related Concept Videos
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Assumptions of Survival Analysis
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time...
Comparing the Survival Analysis of Two or More Groups
Cancer Survival Analysis
Truncation in Survival Analysis
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...

