A modern approach of survival analysis of patients with pancreatic cancer.

Pancreatic cancer is one of the deadliest diseases and becoming an increasingly common cause of cancer mortality. It continues to give rise to massive challenges to clinicians and cancer researchers. One of the main goals of our present study is to determine if there exists any statistically significant difference in the survival probabilities of male and female pancreatic cancer patients in different cancer stages and irrespective of stages. Another goal is to investigate if there exists any parametric probability distribution function that best fits the male and female patient survival times in different stages of cancer, irrespective of stages, and compare the survival probabilities with the non-parametric Kaplan-Meier (KM) method. We employed both parametric and non-parametric statistical approaches to examine the survival probabilities of 10,000 patients diagnosed with pancreatic cancer and showed that there is no significant difference in male and female survival times at any stage except stage IV. We also found no evidence of a statistically significant difference in overall mean survival durations between male and female pancreatic cancer patients, regardless of stage. We used parametric survival analysis and identified the Generalized Pareto (GP) probability distribution as the best fit to the overall survival data for pancreatic cancer patients. Also, we identified the appropriate probability distributions for patients in different cancer stages. We then estimated the overall survival probabilities and compared them with the frequently used non-parametric Kaplan-Meier (KM) survival method, which is not as powerful as our parametric analysis. An assessment of the survival probability estimates generated by the two procedures found that the parametric method produced a better survival probability estimate than the Kaplan-Meier approach. We further compared the median survival times of patients using descriptive, parametric, and non-parametric techniques of analysis and found that the results were relatively consistent. We found that parametric survival analysis is more reliable and efficient than non-parametric Kaplan-Meier estimates since it is based on a well-defined parametric probability distribution.