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Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>11/2017
<mark>Journal</mark>Statistics and Computing
Issue number6
Volume27
Number of pages9
Pages (from-to)1585-1593
Publication statusPublished
Early online date29/09/16
Original languageEnglish

Abstract

The paper considers non-parametric maximum likelihood estimation of the failure time distribution for interval censored data subject to misclassification. Such data can arise from two types of observation scheme; either where observations continue until the first positive test result or where tests continue regardless of the test results. In the former case, the misclassification probabilities must be known, whereas in the latter case joint estimation of the event-time distribution and misclassification probabilities is possible. The regions for which the maximum likelihood estimate can only have support are derived. Algorithms for computing the maximum likelihood estimate are investigated and it is shown that algorithms appropriate for computing non-parametric mixing distributions perform better than an iterative convex minorant algorithm in terms of time to absolute convergence. A profile likelihood approach is proposed for joint estimation. The methods are illustrated on a data set relating to the onset of cardiac allograft vasculopathy in post-heart-transplantation patients.

Bibliographic note

This is the peer reviewed version of the following article: Titman, A.C. (2016) Non-parametric maximum likelihood estimation of interval censored failure time data subject to misclassification Statistics and Computing. DOI:10.1007/s11222-016-9705-7, The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-016-9705-7