Comparison between Parametric and Semi-parametric Cox Models in Modeling Transition Rates of a Multi-state Model: Application in Patients with Gastric Cancer Undergoing Surgery at the Iran Cancer Institute

Background: Research on cancers with a high rate of mortality such as those occurring in the stomachrequires using models which can provide a closer examination of disease processes and provide researcherswith more accurate data. Various models have been designed based on this issue and the present study aimedat evaluating such models. Materials and
Methods: Data from 330 patients with gastric cancer undergoingsurgery at Iran Cancer Institute from 1995 to 1999 were analyzed. Cox-Snell Residuals and Akaike InformationCriterion were used to compare parametric and semi-parametric Cox models in modeling transition ratesamong different states of a multi-state model. R 2.15.1 software was used for all data analyses.
Results: Analysisof Cox-Snell Residuals and Akaike Information Criterion for all probable transitions among different statesrevealed that parametric models represented a better fitness. Log-logistic, Gompertz and Log-normal modelswere good choices for modeling transition rate for relapse hazard (state 1"state 2), death hazard without arelapse (state 1"state 3) and death hazard with a relapse (state 2"state 3), respectively.
Conclusions: Althoughthe semi-parametric Cox model is often used by most cancer researchers in modeling transition rates of multistatemodels, parametric models in similar situations- as they do not need proportional hazards assumptionand consider a specific statistical distribution for time to occurrence of next state in case this assumption is notmade - are more credible alternatives.