Censored count data regression with missing censoring information

Abstract

We investigate estimation in Poisson regression model when the count response is right-censored and the censoring indicators are missing at random. We propose several estimators based on the regression calibration, multiple imputation and augmented inverse probability weighting methods. Under appropriate regularity conditions, we prove the consistency of our estimators and we derive their asymptotic distributions. Simulation experiments are carried out to investigate the finite sample behaviour and relative performance of the proposed estimates. These estimates are illustrated on a real data set.