A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup
- 1 July 2021
- journal article
- research article
- Published by SAGE Publications in Statistical Methods in Medical Research
- Vol. 30 (8), 1890-1903
- https://doi.org/10.1177/09622802211023985
Abstract
Failure time data with a cured subgroup are frequently confronted in various scientific fields and many methods have been proposed for their analysis under right or interval censoring. However, a cure model approach does not seem to exist in the analysis of partly interval-censored data, which consist of both exactly observed and interval-censored observations on the failure time of interest. In this article, we propose a two-component mixture cure model approach for analyzing such type of data. We employ a logistic model to describe the cured probability and a proportional hazards model to model the latent failure time distribution for uncured subjects. We consider maximum likelihood estimation and develop a new expectation-maximization algorithm for its implementation. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed method is examined through simulation studies. An application to a set of real data on childhood mortality in Nigeria is provided.Funding Information
- National Natural Science Foundation of China (11771431, 11690015 and 11901128)
- Research Grant Council of the Hong Kong Special Administration Region (14301918, 14302519)
- National Institutes of Health (R01CA218578)
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