Assessing Misdiagnosis of Relapse in Patients with Gastric Cancer in Iran Cancer Institute Based on a Hidden Markov Multi-state Model

Zare, Ali; Mahmoodi, Mahmood; Mohammad, Kazem; Zeraati, Hojjat; Hosseini, Mostafa; Naieni, Kourosh Holakouie

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자료유형: 학술저널

저자정보: Zare, Ali (Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences) Mahmoodi, Mahmood (Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences) Mohammad, Kazem (Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences) Zeraati, Hojjat (Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences) Hosseini, Mostafa (Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences) Naieni, Kourosh Holakouie (Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences)

저널정보: 아시아태평양암예방학회 Asian Pacific journal of cancer prevention : APJCP Asian Pacific journal of cancer prevention : APJCP 제15권 제9호

발행연도: 2014.1

수록면: 4,109 - 4,115 (7page)

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Background: Accurate assessment of disease progression requires proper understanding of natural disease process which is often hidden and unobservable. For this purpose, disease status should be clearly detected. But in most diseases it is not possible to detect such status. This study, therefore, aims to present a model which both investigates the unobservable disease process and considers the error probability in diagnosis of disease states. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at the Iran Cancer Institute from 1995 to 1999 were analyzed. Moreover, to estimate and assess the effect of demographic, diagnostic and clinical factors as well as medical and post-surgical variables on transition rates and the probability of misdiagnosis of relapse, a hidden Markov multi-state model was employed. Results: Classification errors of patients in alive state without a relapse ($e_{21}$) and with a relapse ($e_{12}$) were 0.22 (95% CI: 0.04-0.63) and 0.02 (95% CI: 0.00-0.09), respectively. Only variables of age and number of renewed treatments affected misdiagnosis of relapse. In addition, patient age and distant metastasis were among factors affecting the occurrence of relapse (state1${\rightarrow}$state2) while the number of renewed treatments and the type and extent of surgery had a significant effect on death hazard without relapse (state2${\rightarrow}$state3)and death hazard with relapse (state2${\rightarrow}$state3). Conclusions: A hidden Markov multi-state model provides the possibility of estimating classification error between different states of disease. Moreover, based on this model, factors affecting the probability of this error can be identified and researchers can be helped with understanding the mechanisms of classification error.

#Classification error #gastric cancer #hidden markov multi-state model #misdiagnosis #relapse

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Zare, Ali

소속기관 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical