Cook, Richard J.Zeng, LeileiLee, Ker-Ai2016-02-102016-02-102008-12http://dx.doi.org/10.1111/j.1541-0420.2007.00978.xhttp://hdl.handle.net/10012/10257This is the peer reviewed version of the following article: Cook, R. J., Zeng, L. and Lee, K.-A. (2008), A Multistate Model for Bivariate Interval-Censored Failure Time Data. Biometrics, 64: 1100–1109. doi: 10.1111/j.1541-0420.2007.00978.x, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2007.00978.x/abstract. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving. The definitive version is available at http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2007.00978.x/abstract’Interval-censored life-history data arise when the events of interest are only detectable at periodic assessments. When interest lies in the occurrence of two such events, bivariate-interval censored event time data are obtained. We describe how to fit a four-state Markov model useful for characterizing the association between two interval-censored event times when the assessment times for the two events may be generated by different inspection processes. The approach treats the two events symmetrically and enables one to fit multiplicative intensity models that give estimates of covariate effects as well as relative risks characterizing the association between the two events. An expectation-maximization (EM) algorithm is described for estimation in which the maximization step can be carried out with standard software. The method is illustrated by application to data from a trial of HIV patients where the events are the onset of viral shedding in the blood and urine among individuals infected with cytomegalovirus.enBivariate failure timeEM algorithmInterval censoringMarkov modelArticle