Bayesian Analysis of Lung Cancer Mortality in the Presence of Misclassification

Mohsen Vahedi; Mohamad Amin Pourhoseingholi; Ahmadreza Baghestani; Alireza Abadi; Sara Sobhi; Zeinab  Fazeli

International Journal of Cancer Management

The Official Journal of Cancer Research Center (CRC), Shahid Beheshti University of Medical Sciences

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Abstract
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Bayesian Analysis of Lung Cancer Mortality in the Presence of Misclassification

Author(s):

Mohsen Vahedi¹,

Mohamad Amin Pourhoseingholi^2,*,

Ahmadreza Baghestani³,

Alireza Abadi³,

Sara Sobhi⁴,

Zeinab Fazeli⁵

1Dept. of Epidemiology and Biostatics, Tehran University of Medical Sciences, Tehran, Iran

2Gastroenterology and Liver diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3Dept. of Biostatistics, Shahid Beheshti University of Medical Sciences, Tehran, Iran

4Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

5Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

International Journal of Cancer Management:Vol. 6, issue Supplement; e80428

Published online:Dec 30, 2013

Article type:Research Article

Received:Oct 15, 2012

Accepted:Dec 20, 2012

How to Cite:Mohsen VahediMohamad Amin PourhoseingholiAhmadreza BaghestaniAlireza AbadiSara SobhiZeinab Fazeliet al.Bayesian Analysis of Lung Cancer Mortality in the Presence of Misclassification.Int J Cancer Manag.6(Supplement):e80428.

Abstract

Background: Lung cancer is an important cause of cancer mortality. Mortality is a familiar projection to address the burden of cancers. However, according to Iranian death registry, about 20% of death statistics were still recorded in misclassified categories. The aim of this study was to estimate lung cancer mortality in Iranian population, using Bayesian approach to revise this misclassification.

Methods: We analyzed National death Statistic reported by the Iranian Ministry of Health and Medical Education from 1995 to 2004. Lung cancer [ICD-10; C34] was expressed as the annual mortality rates/100,000 by sex and by age group. The Bayesian approach to correct and account for misclassification effects in Poisson count regression with a beta prior was employed to estimate the mortality rate of lung cancer in age and sex groups.

Results: According to the Bayesian analysis, there were between 20 to 30 percent underreported mortality records in deaths due to lung cancer, and its mortality rate increased through the recent years.

Conclusion: Our findings suggested a substantial undercount of Lung cancer mortality in Iranian population. Therefore, policy makers who determine research and treatment priorities on death rates should pay more attention to this underreported data.

Keywords

Lung cancer

Mortality

Bayesian analysis

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