Efficient Estimation of the Additive Risks Model for Interval-Censored Data

TL;DR

This paper introduces an efficient maximum-likelihood estimation method for the additive risks model with interval-censored data, utilizing a minorize-maximize algorithm to improve scalability and computational simplicity.

Contribution

It develops a novel MM algorithm for ML estimation of the additive risks model with interval-censored data, enhancing computational efficiency and scalability.

Findings

Simulation studies demonstrate the method's effectiveness.

Application to breast cancer data illustrates practical utility.

The approach simplifies complex likelihood computations.

Abstract

In contrast to the popular Cox model which presents a multiplicative covariate effect specification on the time to event hazards, the semiparametric additive risks model (ARM) offers an attractive additive specification, allowing for direct assessment of the changes or the differences in the hazard function for changing value of the covariates. The ARM is a flexible model, allowing the estimation of both time-independent and time-varying covariates. It has a nonparametric component and a regression component identified by a finite-dimensional parameter. This chapter presents an efficient approach for maximum-likelihood (ML) estimation of the nonparametric and the finite-dimensional components of the model via the minorize-maximize (MM) algorithm for case-II interval-censored data. The operating characteristics of our proposed MM approach are assessed via simulation studies, with illustration on a breast cancer dataset via the R package MMIntAdd. It is expected that the proposed computational approach will not only provide scalability to the ML estimation scenario but may also simplify the computational burden of other complex likelihoods or models.