Comparing the Performance of a Survival Models In a Computerized Dataset

Abstract

Every patient demonstrates the loss of one year of healthy life. In addition, 7.6% of global DALYs are assigned to the neoplasms. The three leading cancers in both sexes worldwide were lung cancer (13% of the total), breast cancer (11.9%), and colorectal cancer (9.7%); the most common types of cancer in men, respectively, are lung cancer (16.8%), prostate cancer (14.8%) and colorectal cancer (10.1%) while in women they are ordered as breast cancer (25.1%), colorectal cancer (9.2%) and lung cancer (8.8%). The research aimed to compare the performance of Weibull, Log-logistics, and Gompertz survival models on oncological data. The research methodology involved the collection of data cases on oncological study analyzed using descriptive statistics, parametric survival models were also used in the analysis. The result shows the maximum likelihood estimates of dataset 1 with 3 with a different model fit, all the information criteria and log-likelihood of the models indicate that Gompertz model has the smallest value in all the information criteria, indicating that Gompertz model is the best-fitted model to Remission Times of Bladder Cancer patient’s data. The research concludes that a parametric that can best be used to model cancer data known as Gompertz model is the best-used model in the research. The research will enable other researchers such as medical personnel to model or know the best model for cancer-related cases.