Analysis of Multivariate Interval Censoring by Diabetic Retinopathy Study

Multivariate failure time data are commonly encountered in biomedical research since each study subject may experience multiple events or because there exists clustering of subjects such that failure times within the same cluster are correlated. In this article, we use the frailty approach to catch the related survival variables and assume each event is a discrete analog as an interval of clinical examinations periodically. For estimation, an Expectation–Maximization (EM) algorithm is developed and is applied to the diabetic retinopathy study (DRS).