Statistical Modelling 6 (2006), 3–22

Bayesian semi-parametric accelerated failure time model for paired doubly interval-censored data

Arnošt Komárek
Biostatistical Center,
Katholieke Universiteit Leuven,
Kapucijnenvoer 35,
B–3000 Leuven
Belgium.
eMail: arnost.komarek@med.kuleuven.be

Emmanuel Lesaffre
Biostatistical Center,
Katholieke Universiteit Leuven.

Abstract:

In this paper, we propose a methodology which a) evaluates the effect of covariates on doubly interval-censored paired responses, b) is based on minimal parametric assumptions concerning the distributional parts of the model and c) evaluates the association between the two responses of the pair. Our methodology tackles three research questions arising from the Signal Tandmobiel® project, a prospective Flemish (Belgian) longitudinal dental study. The research questions are 1) What is the effect of baseline covariates on the time-to-caries of the permanent right first molars? 2) Is the effect of the covariates the same for the upper and lower teeth? 3) What is the association between the times-to-caries on the upper and lower teeth? Time-to-caries is defined as the difference of two interval-censored observations, caries time and emergence time, and hence it is a doubly interval-censored response. We suggest using an accelerated failure time model with a bivariate smooth error distribution being a mixture of bivariate normal components defined on a fine fixed grid. To deal with the problem of doubly interval censoring, we use Bayesian methodology and Markov chain Monte Carlo sampling.

Keywords:

density smoothing; Gaussian Markov random field; Markov chain Monte Carlo; regression; survival data
 

Downloads:

Data and R code in zipped archive

IMPORTANT NOTICE: It is possible to use these data for your research work under the condition that each manuscript is first approved by
Prof. Emmanuel Lesaffre
Biostatistical Centre Katholieke Universiteit Leuven
Kapucijnenvoer 35
B-3000 Leuven
Belgium
eMail: emmanuel.lesaffre@med.kuleuven.be

R and R package bayesSurv are available for download at http://cran.r-project.org.

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