Abstract
Research on cancers with a high rate of mortality such as those occurring in the stomach requires using models which can provide a closer examination of disease processes and provide researchers with more accurate data. Various models have been designed based on this issue and the present study aimed at evaluating such models. Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. Cox-Snell Residuals and Akaike Information Criterion were used to compare parametric and semi-parametric Cox models in modeling transition rates among different states of a multi-state model. R 2.15.1 software was used for all data analyses. Analysis of Cox-Snell Residuals and Akaike Information Criterion for all probable transitions among different states revealed that parametric models represented a better fitness. Log-logistic, Gompertz and Log-normal models were good choices for modeling transition rate for relapse hazard (state 1?state 2), death hazard without a relapse (state 1?state 3) and death hazard with a relapse (state 2?state 3), respectively. Although the semi-parametric Cox model is often used by most cancer researchers in modeling transition rates of multi- state models, parametric models in similar situations- as they do not need proportional hazards assumption and consider a specific statistical distribution for time to occurrence of next state in case this assumption is not made - are more credible alternatives.
Highlights
Gastric cancer is one of the most common causes of cancer deaths all over the world
Analysis of Cox-Snell Residuals and Akaike Information Criterion for all probable transitions among different states revealed that parametric models represented a better fitness
Conclusions: the semi-parametric Cox model is often used by most cancer researchers in modeling transition rates of multistate models, parametric models in similar situations- as they do not need proportional hazards assumption and consider a specific statistical distribution for time to occurrence of state in case this assumption is not made - are more credible alternatives
Summary
Gastric cancer is one of the most common causes of cancer deaths all over the world. Every year, more than thousand new cases are reported throughout the world and more than 650 thousand people die from this type of cancer (Parkin, 1998). Asian Pacific Journal of Cancer Prevention, Vol 14, 2013 consider a specific probability distribution for time to the occurrence of state; it is the most useful model in modeling transition rates of multi-state models (Hougaard, 1999; Yagi et al, 2000; Andersen and Keiding, 2002; Adachi et al, 2003; Buonadonna et al., 2003; Chau et al, 2004; Zeraati et al, 2005; Dehkordi and Tabatabaee, 2007; Biglarian et al, 2009). This model is severely affected by proportional hazards assumption and, for this reason, is often called Cox proportional hazard model. It should be noted that the Weibull and exponential models are the only ones that have, both, the PH and AFT features
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