Statistical Models for Interval-Censored Time-to-event data

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientific

Abstract

Time-to-event data are collected and studied in many research fields, such as medical science and reliability theory. A complication often encountered with these data is censoring. The time of the actual event is not observed: for each subject, only an interval can be observed that contains this time. Parametric, as well as nonparametric estimation, procedures can be employed to estimate the relevant quantities of interest. Also, various models can be used to include explanatory variables in the model. In this paper the parametric and nonparametric approach to interval-censored data are described. The aim is to show a glimpse of the possibilities of stochastic modelling and stimulate discussion on the development of models specifically in the context of ageing.

Original languageEnglish
Title of host publicationThe Ageing of Materials and Structures
Subtitle of host publicationTowards Scientific Solutions for the Ageing of Our Assets
EditorsKlaas van Breugel, Dessi Koleva, Ton van Beek
Place of PublicationCham
PublisherSpringer
Pages237-244
Number of pages8
ISBN (Electronic)978-3-319-70194-3
ISBN (Print)978-3-319-70192-9
DOIs
Publication statusPublished - 2018

Keywords

  • Accelerated life
  • EM algorithm
  • Maximum likelihood
  • Cox proportional hazard

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