A two-sample test with interval censored data via multiple imputation

Interval censored data arise naturally in large scale panel studies where subjects can only be followed periodically and the event of interest can only be recorded as having occurred between two examination times. In this paper we consider the problem of comparing two interval-censored samples. We propose to impute exact failure times from interval-censored observations to obtain right censored data, then apply existing techniques, such as Harrington and Fleming's G(ρ) tests to imputed right censored data. To appropriately account for variability, a multiple imputation algorithm based on the approximate Bayesian bootstrap (ABB) is discussed. Through simulation studies we find that it performs well. The advantage of our proposal is its simplicity to implement and adaptability to incorporate many existing two-sample comparison techniques for right censored data. The method is illustrated by reanalysing the Breast Cosmesis Study data set.