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Prognostic Factors for Recurrence And Survival of Patients with Breast Cancer Using A Multi-state Model
Maryam Rastegar1,2, Zahra Arab Borzu2, Ahmad Reza Baghestani3
1Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
Galen Medical Journal
|April 27, 2026
Summary
Multi-state models help analyze breast cancer patient outcomes. This study shows transition probabilities and mean sojourn times are crucial for predicting recurrence and death, aiding clinical care decisions.
Area of Science:
- Oncology
- Biostatistics
Background:
- Medical studies frequently involve patient events, often analyzed using multi-state models.
- Breast cancer recurrence and mortality are significant concerns in patient management.
Purpose of the Study:
- To investigate the impact of risk factors and transition probability on recurrence and death in breast cancer patients.
- To apply multi-state models for analyzing breast cancer patient outcomes.
Main Methods:
- Retrospective cohort study of 814 women with breast cancer (Yazd province, Iran, 2004-2016).
- Data analyzed using a multi-state model in R programming language.
Main Results:
- 5% of patients recovered, while 20.7% died after initial treatment.
- First-year transition probabilities: 1.4% (treatment to recovery), 17% (treatment to death), 29% (recovery to death).
- Mean sojourn times were 2.93 years (treatment) and 9.8 years (recovery).
Conclusions:
- Multi-state models effectively predict disease transition probabilities.
- Transition probabilities, mean sojourn times, and hazard ratios inform clinical care for breast cancer patients.
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