June 2023 A Bayesian accelerated failure time model for interval censored three-state screening outcomes
Thomas Klausch, Eddymurphy U. Akwiwu, Mark A. van de Wiel, Veerle M. H. Coupé, Johannes Berkhof
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Ann. Appl. Stat. 17(2): 1285-1306 (June 2023). DOI: 10.1214/22-AOAS1669

Abstract

Women infected by the human papillomavirus are at an increased risk to develop cervical intraepithelial neoplasia lesions (CIN). CIN are classified into three grades of increasing severity (CIN-1, CIN-2, and CIN-3) and can eventually develop into cervical cancer. The main purpose of screening is detecting CIN-2 and CIN-3 cases which are usually removed surgically. Screening data from the POBASCAM trial involving 1454 HPV-positive women are analyzed with two objectives, estimate: (a) the transition time from HPV diagnosis to CIN-3 and (b) the transition time from CIN-2 to CIN-3. The screening data have two key characteristics. First, the CIN state is monitored in an interval censored sequence of screening times. Second, a woman’s progression to CIN-3 is only observed if the woman progresses to, both, CIN-2 and from CIN-2 to CIN-3 in the same screening interval. We propose a Bayesian accelerated failure time model for the two transition times in this three-state model. To deal with the unusual censoring structure of the screening data, we develop a Metropolis-within-Gibbs algorithm with data augmentation from the truncated transition time distributions.

Funding Statement

Johannes Berkhof was supported by the European Union Horizon 2020 Framework Programme for Research and Innovation, RISCC project (grant number 847845).

Acknowledgments

The authors would like to thank four anonymous referees, an Associate Editor, and the Editor for their diligent efforts and insightful comments that helped to improve the manuscript. The authors also thank Professor C. J. L. M. Meijer for providing the POBASCAM data.

Citation

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Thomas Klausch. Eddymurphy U. Akwiwu. Mark A. van de Wiel. Veerle M. H. Coupé. Johannes Berkhof. "A Bayesian accelerated failure time model for interval censored three-state screening outcomes." Ann. Appl. Stat. 17 (2) 1285 - 1306, June 2023. https://doi.org/10.1214/22-AOAS1669

Information

Received: 1 March 2021; Revised: 1 July 2022; Published: June 2023
First available in Project Euclid: 1 May 2023

MathSciNet: MR4582713
zbMATH: 07692383
Digital Object Identifier: 10.1214/22-AOAS1669

Keywords: AFT , Bayesian , interval censoring , multistate , Screening , semi-Markov , survival

Rights: Copyright © 2023 Institute of Mathematical Statistics

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