Evidence map›Paper›PMID 42781478›Full record

ArticleiScience2026

Predicting disease progression and mortality in prostate cancer using real-world data-driven time-inhomogeneous Markov models.

Jiaqi Wang, Yuanshi Jiao, Dawn Craig, Steven Wai Kwan Siu, Lei Si, David Makram Bishai, Yi Yang, Yingyao Chen, Qingpeng Zhang, Rong Na and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jiaqi WangCentre 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.
Yuanshi JiaoCentre 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.
Dawn CraigPopulation Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
Steven Wai Kwan SiuDepartment of Clinical Oncology, Queen Mary Hospital, Hong Kong SAR, China.
Lei SiSchool of Health Sciences, Western Sydney University, Penrith, NSW, Australia.
David Makram BishaiDivision of Health Economics, Policy and Management, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Yi YangNational Health Commission Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China.
Yingyao ChenNational Health Commission Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China.
Qingpeng ZhangHKU Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong SAR, China.
Rong NaDepartment of Surgery, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Xue LiCentre 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

health economicsprognosisprostate cancertime-inhomogeneous Markov model

Identifiers

PMID42781478
PMCPMC13599639

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.