Exponential Model for Assessing Competing Risk in Patient Survival Time in Clinical Trials

Abstract Patients’ survival time depends on several concomitant variables that compete for cancer patients’ survival. Censored values make parameter estimation difficult. For this reason, an optimal model was developed to estimate patients’ paucity entries that generate censored values in survival data. A total of 98 cancer patients were followed to death and their survival times recorded. The data was made up of 80% censored values and 20% uncensored values. The average survival time for patients is 46 months. The presence of tumor in the breast cancer contributed to six times the death of the patients. Simulations show that, median follow-up time is 4.17874 months and the density of incidence of the risk of cancer is 0.0757.