Bayesian analysis of lung cancer mortality in the presence of misclassification

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 categoryies. 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.