The current duration design for estimating the time to pregnancy distribution: a nonparametric Bayesian perspective
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© 2015 Springer Science + Business Media. This is the post print version of the following article: Gasbarra, Dario & Arjas, Elja & Vehtari, Aki & Slama, Rémy & Keiding, Niels. 2015. The current duration design for estimating the time to pregnancy distribution: a nonparametric Bayesian perspective. Lifetime Data Analysis. Volume 21, Issue 4. 594-625. ISSN 1380-7870 (printed). DOI: 10.1007/s10985-015-9333-0, which has been published in final form at http://link.springer.com/article/10.1007/s10985-015-9333-0.
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School of Science |
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2015
Major/Subject
Mcode
Degree programme
Language
en
Pages
594-625
Series
Lifetime Data Analysis, Volume 21, Issue 4
Abstract
This paper was inspired by the studies of Niels Keiding and co-authors on estimating the waiting time-to-pregnancy (TTP) distribution, and in particular on using the current duration design in that context. In this design, a cross-sectional sample of women is collected from those who are currently attempting to become pregnant, and then by recording from each the time she has been attempting. Our aim here is to study the identifiability and the estimation of the waiting time distribution on the basis of current duration data. The main difficulty in this stems from the fact that very short waiting times are only rarely selected into the sample of current durations, and this renders their estimation unstable. We introduce here a Bayesian method for this estimation problem, prove its asymptotic consistency, and compare the method to some variants of the non-parametric maximum likelihood estimators, which have been used previously in this context. The properties of the Bayesian estimation method are studied also empirically, using both simulated data and TTP data on current durations collected by Slama et al. (Hum Reprod 27(5):1489–1498, 2012).Description
Keywords
McMC, Posterior consistency, Data augmentation, Logistic process prior, Generalized gamma convolution process
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Citation
Gasbarra, Dario & Arjas, Elja & Vehtari, Aki & Slama, Rémy & Keiding, Niels. 2015. The current duration design for estimating the time to pregnancy distribution: a nonparametric Bayesian perspective. Lifetime Data Analysis. Volume 21, Issue 4. 594-625. ISSN 1380-7870 (printed). DOI: 10.1007/s10985-015-9333-0.