Assessing Efficient Risk Ratios: An Application to Surgical Stage Prediction in Cervical Cancer

Abstract

Background: Cervical cancer remains the second most commonly diagnosed cancer and the third leading cause of cancer death in developing countries . Improving clinicians’ knowledge and understanding of surgical staging is critical in the fight against the disease. However, a systematic evaluation of different ordinal regression models based on diverse predicted outcomes has not been given its due share in literature. Objective: To systematically assess the flexibility of odds ratios for three popular ordinal regression models i.e. the Multinomial Logistic (ML) model, the Continuation Ratio (CR) model and Adjacen t Category Logistic (ACL) model when applying cervical cancer data in surgical stage prediction. Method: We systematically, compared the performanc e of CR, ML and the ACL as the predictive mechanisms, and evaluate the most appropriate model in the cervical cancer setting. The study considered women who visited the Oncology department at the Moi Teaching and Referral Hospital’s Chandaria Cancer and Chronic Diseases Center and were diagnosed and surgically treated for cervical cancer from January 2014 to December 2018 . Results and Conclusion: We presented the comparison between 3 different regression models for ordinal data within the cervical cancer set ting. We found that the CR model without proportional odds yielded better results comparing Akaike Information Criterion (AIC), log likelihood ratio and residual deviance. In addition, the key prognostic factor associated with invasive cervical cancer was the (International Federation of Gynecology and Obstetrics) FIGO clinical stage which in particular, had a higher influence on the surgical Stage 2 outcomes compared to the lesser surgical stage categories. All the 5 independent features selected for classifying the patients into surgical stages were the FIGO clinical stage and partly, the presence or absence of sympto matic vaginal discharge.