Abstract
A classical problem in survival analysis is to estimate the failure time distribution from right-censored observations obtained from an incident cohort study. Frequently, however, failure time data comprise two independent samples, one from an incident cohort study and the other from a prevalent cohort study with follow-up, which is known to produce length-biased observed failure times. There are drawbacks to each of these two types of study when viewed separately. We address two main questions here: (i) Can our statistical inference be enhanced by combining data from an incident cohort study with data from a prevalent cohort study with follow-up? (ii) What statistical methods are appropriate for these combined data? The theory we develop to address these questions is based on a parametrically defined failure time distribution and is supported by simulations. We apply our methods to estimate the duration of hospital stays.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: The first author was supported by a Natural Sciences and Engineering Research Council of Canada PGSD-3 award. David Stephens acknowledges the support of a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC).
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Supplementary material
The online version of this article offers supplementary material (https://doi.org/10.1515/ijb-2020-0042).
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