Rank-Based Inference for the Accelerated Failure Time Model with Partially Interval-Censored Data

This paper presents a unified rank-based inferential procedure for fitting the accelerated failure time model to partially interval-censored data.A Gehantype monotone estimating function is constructed based on the idea of the familiar weighted log-rank test, and an extension to a general class of rank-based estimating functions is suggested.The proposed estimators can be obtained via linear programming and are shown to be consistent and asymptotically normal via standard empirical process theory.Unlike common maximum likelihood-based estimators for partially interval-censored regression models, our approach can directly provide a regression coefficient estimator without involving a complex nonparametric estimation of the underlying residual distribution function.An efficient variance estimation procedure for the regression coefficient estimator is considered.Moreover, we extend the proposed rank-based procedure to the linear regression analysis of multivariate clustered partially interval-censored data.The finite-sample operating characteristics of our approach are examined via simulation studies.Data example from a colorectal cancer study illustrates the practical usefulness of the method.