Kaplan-Meyer Survival Curves: Simulation Technique

Abstract

The right censoring of survival data, being the most conventional method of research, is analyzed. The patient survival is explored in a time span that is shorter in fact than the actual survival time. However, when the actual survival time is unknown, the proxy of the observable survival time will be used for estimating the actual survival time. 
 The algorithm for estimation of survival probabilities is demonstrated by data on 20 patients during six months, with visualizing the technique of simulating Kaplan – Meyer curves by categorical variables (method of treatment and gender) using GraphPad Prism software for statistical data processing. It is argued that Kaplan – Meyer curves could provide an effective tool in simulating the patient survival in case of COVID-19 by various criteria of grouping: gender (male and female); treatment method; associated diseases (diabetes and others); age group; vaccinated or not vaccinated patients etc. 
 The significance of differences between survival curves of patienst in various groups can be found using Log-Rank test, Gehan – Wilcoxon test, Mantel – Cox test and others. The results of tests produced on the basis of data on 42 patients ill with leukemia show significant differences in the survival between two groups of patients. This confirms the assumption that the new method of treatment is more effective than the conventional one. The main deficiency of the nonparametric method of Kaplan – Meyer is that it is incapable to build curves by several categorical variables. The main advantages of Cox regression based on the Cox proportional hazards model are demonstrated.