Background: Accurate assessment of disease progression requires proper understanding of natural diseaseprocess 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 whichboth investigates the unobservable disease process and considers the error probability in diagnosis of diseasestates. Materials and
Methods: Data from 330 patients with gastric cancer undergoing surgery at the IranCancer 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 probabilityof misdiagnosis of relapse, a hidden Markov multi-state model was employed.
Results: Classification errors ofpatients in alive state without a relapse (e21) and with a relapse (e12) 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 ofrelapse. In addition, patient age and distant metastasis were among factors affecting the occurrence of relapse(state1"state2) while the number of renewed treatments and the type and extent of surgery had a significanteffect on death hazard without relapse (state2"state3)and death hazard with relapse (state2"state3).
Conclusions:A hidden Markov multi-state model provides the possibility of estimating classification error between differentstates of disease. Moreover, based on this model, factors affecting the probability of this error can be identifiedand researchers can be helped with understanding the mechanisms of classification error.