A Multi-State Model for Lung Cancer Mortality in Survival Progression

Lung cancer remains one of the leading causes of death worldwide due to its high rates of illness and mortality. In this study, we applied a continuous-time multi-state Markov model to examine how lung cancer progresses through six clinically defined stages, using retrospective data from 576 patients. The model describes movements between disease stages and the final stage (death), providing estimates of how long patients typically remain in each stage and how quickly they move to the next. It also considers important demographic and clinical factors such as age, smoking history, hypertension, asthma, and gender, which influence survival outcomes. Our findings show slower changes at the beginning of the disease but faster decline in later stages, with clear differences across patient groups. This approach highlights the dynamic course of the illness and can help guide tailored follow-up, personalized treatment, and health policy decisions. The study is based on a secondary analysis of publicly available data and therefore did not require clinical trial registration.

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Stats
Published
2025-11-05
DOI
https://doi.org/10.3390/stats8040106
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
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article

A Multi-State Model for Lung Cancer Mortality in Survival Progression

Vinoth Raman, Sandra S. Ferreira, Dário Ferreira, Ayman Alzaatreh
Stats
Lung Cancer Diagnosis and Treatment
article

A Multi-State Model for Lung Cancer Mortality in Survival Progression

Vinoth Raman, Sandra S. Ferreira, Dário Ferreira, Ayman Alzaatreh
article en

Abstract

Lung cancer remains one of the leading causes of death worldwide due to its high rates of illness and mortality. In this study, we applied a continuous-time multi-state Markov model to examine how lung cancer progresses through six clinically defined stages, using retrospective data from 576 patients. The model describes movements between disease stages and the final stage (death), providing estimates of how long patients typically remain in each stage and how quickly they move to the next. It also considers important demographic and clinical factors such as age, smoking history, hypertension, asthma, and gender, which influence survival outcomes. Our findings show slower changes at the beginning of the disease but faster decline in later stages, with clear differences across patient groups. This approach highlights the dynamic course of the illness and can help guide tailored follow-up, personalized treatment, and health policy decisions. The study is based on a secondary analysis of publicly available data and therefore did not require clinical trial registration.

StatsVol. 8(4)
University of Beira Interior (PT), American University of Sharjah (AE), Imam Abdulrahman Bin Faisal University (SA)
Openalex Percentile: Top 53%
Lung Cancer Diagnosis and Treatment
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