Journal of Semnan University of Medical Sciences
Image Credit:Koomesh
Outline
Application of log-normal parametric model in disability structure to predict metastasis and death due to breast cancer
Authors
Abstract
Introduction: Determining disease progression process and its affecting factors are of the most important issues in controlling the disease. This study aimed to predict the breast cancer progression as well as assessing the relationship between demographical and clinical factors. Materials and Methods: This retrospective cohort study was conducted on 527 Iranian females with breast cancer who underwent surgery, from 1995 to 2013 using checklists. The effect of the factors on death and tumor recurrence was assessed by log-normal model fitted into each transition of illness-death model which were used to investigate the relationship between demographic and clinical factors and survival time. Data analysis was performed using statistical R software version 3.1.1. The significance level of 0.05 was considered. Results: Evaluating the hazard of death without recurrence, the risk of death in patients over 50 years were higher than those under 50 (P=0.01). A tumor size of 2-5 cm was introduced as a death factor in recurrent patients (P=0.01).Age and type of tumor did not impact the hazard. Log-normal distribution was chosen for downtime between steps. Conclusion: Based on the results, age at diagnosis had significant impact on the risk of death before the first recurrence. Tumor size had a significant effect on death after tumor recurrence. In addition, Log-normal and disability models are appropriate tools to identify the factors influencing survival of patients with breast cancer.
Highlights
References
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Copyright
© 2016, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.
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