A model for a binary variable with time-censored observations

Abstract

A binary variable can specify the incidence of a particular disease, Y = 1 or a lifetime free of the disease, Y = 0. In a study, some subjects have Y = 1 recorded at specified ages. For other subjects, with Y unknown due to incomplete follow-up, the observed follow-up time compared with the ma1 incidence pattern for the disease gives some information on the possibility that Y = 0. A model for this situation is proposed which combines a logistic relationship for the probability of incidence and an exponential distribution for the time of incidence. The efficiency of the model is compared with that of a logistio model with no time censoring. The behaviour of a logistic model applied without considering the time censoring is also examined.