A mixture model for bivariate interval-censored failure times with dependent susceptibility
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Date
2020-03-07
Authors
Jiang, Shu
Cook, Richard J.
Advisor
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
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.
Description
The final publication of this article: Shu Jiang and Richard J. Cook, A mixture model for bivariate interval-censored failure times with dependent susceptibility, Statistics in Biosciences (2020), 12: 37–62 is available at Springer via https://doi.org/10.1007/ s12561-020-09270-7.
Keywords
copula, estimating functions, interval-censored, multivariate, nonsusceptible, twostage- estimation