An Integrative Bayesian Model Analysis of Patient Characteristics and Treatment Variables to Understand Lung Cancer Survival Rates in Kerman Province, Iran

Javad Ghasemi; Mitra Samareh Fekri; Mohammad Hasan Larizadeh; Shahriar Dabiri; Yunes Jahani

doi:10.18502/jbe.v8i4.13357

Javad Ghasemi HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Mitra Samareh Fekri Cardiovascular Research Center, Institute of Basic and Clinical Physiology Sciences, Kerman University of Medical Sciences, Kerman, Iran.
Mohammad Hasan Larizadeh Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of medical sciences, Kerman, Iran.
Shahriar Dabiri Pathology and Stem Cell Research Center, Department of Pathology, Afzalipour School of Medicine, Kerman University of Medical Sciences, Kerman, Iran.
Yunes Jahani Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran.

DOI: https://doi.org/10.18502/jbe.v8i4.13357

Keywords: Lung cancer; Bayesian; Survival rate

Abstract

Introduction: Lung cancer (LC) is the most common type of cancer and causes of death among males. This study aims to estimate the survival rate of lung cancer patients by employing the benefits of Bayesian modeling in determining factors affecting the survival of lung cancer in Kerman province, Iran.

Methods: We conducted a historical cohort study of 195 patients with lung cancer from 2016 to 2018. In this study, we used linear dependent Dirichlet process (LDDP), and employed some results of the previous study as informative prior for better estimation.

Results: Of the 195 patients, 160 died. The mean age of patients at the time of diagnosis was 62.43±12.55. The median survival time of patients was 10.4 months. Men accounted for 75.9% of the total patients. One, two, and three-year survival rate was 44.5%, 22.9%, and 16.4%, respectively. The multivariable model results showed that treatments were significant. Other variables had no significant effect.

Conclusion: Our study highlights the importance of prompt diagnosis and appropriate treatment in improving the survival rate of lung cancer patients. We found that patients who received at least one usual lung cancer treatment, such as chemotherapy, radiation therapy, or surgery, had higher survival rates compared to those who did not receive any treatment. While our study has some limitations, such as its retrospective design, our use of Bayesian modeling techniques allowed us to effectively incorporate prior information from previous studies to improve estimation accuracy.