Skip to content
Research Article Open access CC BY 4.0

Multi-state and Semi-Markov Modelling of Prostate Cancer Disease Trajectories and Mortality Dynamics

Francis Ayiah-Mensah, Senyefia Bosson-Amedenu, Emmanuel Harris, Mohammed Frempong, Rebecca Nduba. Arhin

International Research Journal of Oncology · pp. 310–326 · Published 1 Jul 2026

10.9734/irjo/2026/v9i2218

Abstract

Background: Previous prostate cancer research has often been conducted in clinical or laboratory settings and has relied on traditional survival models that focus on a single disease endpoint. Such approaches do not fully account for intermediate disease states, transition risks, or disease-state occupancy times. This study aimed to model the dynamic progression and survival of prostate cancer using continuous-time Multi-State Markov and Semi-Markov frameworks. Methods: A retrospective longitudinal study was conducted using clinical data from 300 patients with prostate cancer. The dataset included cancer stage, treatment exposure, tumour characteristics, survival outcomes and follow-up duration. Three disease states or stages were considered: Early-stage disease, Advanced-stage disease and Death. Transition intensities, transition probabilities, mean sojourn times and transition-specific hazard ratios were estimated using Continuous-Time Multi-State and Semi-Markov models. Results: The largest proportion of patients had Stage II disease (30.00%), followed by Stage III disease (27.67%). The transition intensity from Early-stage disease to Advanced-stage disease (0.1603) was higher than the direct transition from Early-stage disease to Death (0.0724), suggesting a sequential disease progression pattern. Mean sojourn time was shorter in Early-stage disease (4.297 months) than in Advanced-stage disease (93.924 months). At 60 months, 61.90% of patients initially in Early-stage disease had transitioned to Death. Increasing age was associated with mortality among patients with Advanced-stage disease (HR = 1.013), whereas radiation therapy was associated with a lower mortality hazard in this group (HR = 0.721). Conclusion: The multi-state modelling framework provided a structured representation of prostate cancer progression by accounting for intermediate disease states, transition-specific risks, state occupancy times and treatment-related effects.

Prostate cancer multi-state modelling Semi-Markov model disease progression transition probabilities transition intensity sojourn time.

Cited by 0

No indexed citations yet.

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

0

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.