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, CIN2 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.
Original language | English |
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Pages (from-to) | 1285-1306 |
Number of pages | 22 |
Journal | Annals of Applied Statistics |
Volume | 17 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2023 |
Keywords
- AFT
- Bayesian
- interval censoring
- multistate
- screening
- semi-Markov
- survival