A Mixture Model for Bivariate Interval-Censored Failure Times with Dependent Susceptibility

Shu Jiang () and Richard J. Cook
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Shu Jiang: University of Waterloo
Richard J. Cook: University of Waterloo

Statistics in Biosciences, 2020, vol. 12, issue 1, No 3, 37-62

Abstract: Abstract Interval-censored failure times arise when the status with respect to an event of interest is only determined at intermittent examination times. In settings where there exists a sub-population of individuals who are not susceptible to the event of interest, latent variable models accommodating a mixture of susceptible and nonsusceptible individuals are useful. We consider such models for the analysis of bivariate interval-censored failure time data with a model for bivariate binary susceptibility indicators and a copula model for correlated failure times given joint susceptibility. We develop likelihood, composite likelihood, and estimating function methods for model fitting and inference, and assess asymptotic-relative efficiency and finite sample performance. Extensions dealing with higher-dimensional responses and current status data are also described.

Keywords: Copula; Estimating functions; Interval-censored; Multivariate; Nonsusceptible; Two-stage estimation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12561-020-09270-7

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