Evaluation of competing risks based on both dependent, independent real and simulated data by using self developed R program

Evaluation of Competing Risks Based On Both Dependent , Independent Real And Simulated Data By Using Self Developed R Program Classical survival analysis methods commonly evaluate single cause of failure (die, relapse, etc.). However, failures may be of several distinct causes and the observation of any one of them prevents the observation in others. In this situation, survival analysis must be done by considering competing risks. In survival analysis when several competing risks exist, Kaplan Meier (KM) method ignores the competing risks (CR) aspect of the data. In order to show KM approach gives insufficient results and inferences when there is competing risks, we studied mean differences in survival probability which are both analyzed by CR and KM approaches separately in real data set and simulated data sets. During creation of simulated data, we used different sample sizes (n=100, 150, 250, 500, 1000.) in two different distributions (weibull and exponentials distributions) so that different scenarios are presented to give corresponding outcomes and inferences. Outcomes and graphs for Cumulative hazard function are presented. We used Grays test statistics to study covariate effects when competing risks exists. Both real and simulated data sets shows that when there is competing risks competing risks approach gives more appropriate results than classical survival analysis. When weibull distributions are used mean differences in survival probabilities of two methods are different but not effected by sample sizes. But when exponentials distributions are used mean differences in survival probabilities depend on censoring rate and sample sizes. When censoring rates gets higher we observed that mean differences in survival probabilities of two methods is decreased. As the sample sizes gets higher we see that mean differences in survival probabilities of two methods is increased. R statistical software is used to simulate and analyze data sets. Keywords: R program, Competing Risks, Exponential Distribution, Cumulative Hazard Function, Gray's models