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Updated: Sep 26, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Predicting disease progression and mortality in prostate cancer using real-world data-driven time-inhomogeneous
Jiaqi Wang1, Yuanshi Jiao1,2, Dawn Craig3
1Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Abstract:
Prostate cancer progression varies across patients. Understanding disease trajectories and mortality risk is important for improved healthcare. This study developed a time-inhomogeneous Markov model using territory-wide electronic medical records from Hong Kong to estimate 10-year disease progression. The model was constructed using data from 3,274 patients newly diagnosed in 2010-2012. Their electronic medical records defined baseline covariates (age, Charlson Comorbidity Index [CCI], and prostate-specific antigen [PSA] level) and time-varying health states. Mild cases (age≤65, CCI = 0, PSA≤4 ng/mL) were predicted to have 16.9% and 28.0% mortality at five and ten years. Evaluated on an independent 2013 cohort, our model showed strong prediction performance for metastasis-free survival (concordance index [C-index]: 0.780; 95% confidence interval [CI]: 0.723-0.837) and overall survival (C-index: 0.787; 95% CI: 0.731-0.843), exceeding Cox proportional hazards and random survival forest models. This study provides a pragmatic tool for long-term progression risk prediction and health economic evaluation.
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