Abstract

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.

Information

Preprint No.SS-2024-0003
Manuscript IDSS-2024-0003
Complete AuthorsTaehwa Choi, Sangbum Choi, Dipankar Bandyopadhyay
Corresponding AuthorsDipankar Bandyopadhyay
Emailsbandyopd@gmail.com

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Acknowledgments

The colorectal cancer data was derived based on raw data sets obtained from

Data Sphere, LLC. The authors acknowledge support from grant (RS-2024-

00340298) from the National Research Foundation of S. Korea (T. Choi),

grants (2022R1A2C1008514, 2022M3J6A1063595) from the National Research Foundation of S. Korea and grant (K2305261) from Korea University

(S. Choi), and grants (P20CA252717, P20CA264067, and R21DE031879)

from the United States National Institutes of Health (D. Bandyopadhyay).

Supplementary Materials

Proofs of Theorems 1 and 2, details on estimation under the DC setup, and

additional simulation studies are relegated to the Supplementary Material.

Supplementary materials are available for download.