A Bayesian accelerated failure time model for interval censored three-state screening outcomes

TL;DR

This paper introduces a Bayesian accelerated failure time model tailored for interval-censored three-state screening data, specifically analyzing HPV-related cervical lesion progression, with a novel algorithm to handle complex censoring structures.

Contribution

It develops a new Bayesian model and a Metropolis-within-Gibbs algorithm to accurately estimate transition times in interval-censored three-state screening data.

Findings

Successfully estimates transition times from HPV to CIN-3 and CIN-2 to CIN-3.

Handles complex interval censoring and state observation conditions.

Provides a robust statistical framework for cervical cancer screening analysis.

Abstract

Women infected by the Human papilloma virus are at an increased risk to develop cervical intraepithalial 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 surgically removed. Screening data from the POBASCAM trial involving 1,454 HPV-positive women is 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.