Abstract Survival Analysis

Abstract Survival analysis is normally used to describe the analysis of data that depend on the time between constant starting point until the occurrence of specific event or endpoint. There are certain aspects of the survival analysis data, such as censoring and non-normality, which can generate great difficulty when trying to analyze data using traditional statistical models. In this paper, the Kaplan-Meier estimator is used to estimate the survival function. Also Log-Rank test is used to compare between groups using the stages of the illness as a comparing factors. Finally, Cox’s regression model is one of the most applied methods in medical research, is used to determine the factors that affect the survival time and assess the relative

diagnosis of deadly diseases) until the occurrence of a specific event (i.e. death) or endpoint [16]. Survival analysis is called reliability analysis in engineering, duration analysis in economics, event history analysis in sociology and medical researchers give it the name of survival analysis. Survival analysis is used because the survival data are generally not symmetrically distributed, so it can not follow the normal distribution. The survival data depend on the time and it render standard methods in which the survival times are frequently censored [1]. Censored data arises when the study end and the patient still alive, a patient is lost to follow-up during the study period, and, if a patient is experience event but not the event of interest. The feature that characterizes such data is that the event not necessarily has occurred in all patients when the time of study ends. In addition, full survival time for patients can be unknown [2]. The survival time often called a failure time or event time is a time interval between a starting point and a subsequent event

The time period in which an individual is in the study is known as the study time. Patient time is the period of time spent by the patient since the diagnosis of the disease till the occurrence of the event or the last follow up. This time includes the study time [7]. to illustrate this difference take the following example for seven patients in the study of breast cancer, patients were followed up from the date of diagnosis until death due to breast cancer or last follow up. the Figure 1.a Illustrates entry of patients during the period of the study the symbol ((▌ refers to that, the letter (D) indicates the occurrence of the event (i.e death), while the symbols (L) refers to the last follow-up, and (A) indicates that the patient is still alive after the end of the study. As for figure 1.b, the data are displayed in the form of survival analysis where death is the event of interest ,the letter (D) indicates the occurrence of the event like the patients 2, 6 and 7, and the patients who did not die and the last follow-up, are considered right-censored (C) such as 1,3,4 and