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

TL;DR

This paper introduces a penalized likelihood method for semiparametric additive hazard models that accounts for cured individuals in survival data, improving bias correction in the presence of interval censoring.

Contribution

It develops a novel estimation approach using a primal-dual interior point algorithm for constrained maximum likelihood in cure models with additive hazards.

Findings

Effective estimation of cure models with additive hazards

Handles partly interval-censored data

Provides bias-reducing estimates

Abstract

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.