Efficient Estimation of Mixture Cure Frailty Model for Clustered Current Status Data

TL;DR

This paper introduces a unified semiparametric methodology using EM algorithm and penalized splines to analyze clustered current status data with a cure fraction, accounting for within-subject correlation, supported by asymptotic theory and practical R implementation.

Contribution

It develops a novel estimation approach for clustered current status data with cure fraction, integrating frailty effects and non-parametric modeling, with theoretical guarantees and software tools.

Findings

Method performs well in simulations.

Application to oral health data demonstrates utility.

Provides diagnostic tools for influential observations.

Abstract

Current status data abounds in the field of epidemiology and public health, where the only observable data for a subject is the random inspection time and the event status at inspection. Motivated by such a current status data from a periodontal study where data are inherently clustered, we propose a unified methodology to analyze such complex data. We allow the time-to-event to follow the semiparametric GOR model with a cure fraction, and develop a unified estimation scheme powered by the EM algorithm. The within-subject correlation is accounted for by a random (frailty) effect, and the non-parametric component of the GOR model is approximated via penalized splines, with a set of knot points that increases with the sample size. Proposed methodology is accompanied by a rigorous asymptotic theory, and the related semiparametric efficiency. The finite sample performance of our model parameters are assessed via simulation studies. Furthermore, the proposed methodology is illustrated via application to the oral health data, accompanied by diagnostic checks to identify influential observations. An easy to use R package CRFCSD is also available for implementation.