# Mixture cure semiparametric additive hazard models under partly interval censoring -- a penalized likelihood approach

arXiv: https://arxiv.org/abs/2401.01234 · HTML: https://mlsanity.com/paper/2401.01234/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2024.

## Built on by (most cited first, 4 of 4)

- [NNGPT: Rethinking AutoML with Large Language Models](https://mlsanity.com/paper/2511.20333/) (2025)
- [Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models](https://mlsanity.com/paper/2504.13825/) (2025)
- [FABRIC: Framework for Agent-Based Realistic Intelligence Creation](https://mlsanity.com/paper/2510.17995/) (2025)
- [RAGent: Physics-Aware Agentic Reasoning for Training-Free mmWave Human Activity Recognition](https://mlsanity.com/paper/2603.27571/) (2026)

## Abstract (arXiv)

Survival analysis can sometimes involve individuals who will not experience the event of interest, forming what is known as the cured group. Identifying such individuals is not always possible beforehand, as they provide only right-censored data. Ignoring the presence of the cured group can introduce bias in the final model. This paper presents a method for estimating a semiparametric additive hazards model that accounts for the cured fraction. Unlike regression coefficients in a hazard ratio model, those in an additive hazard model measure hazard differences. The proposed method uses a primal-dual interior point algorithm to obtain constrained maximum penalized likelihood estimates of the model parameters, including the regression coefficients and the baseline hazard, subject to certain non-negativity constraints.