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Modeling Survival Data by Using Kaplan Meier Survival Estimates and Log Rank Test
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Modeling Survival Data by Using Kaplan Meier Survival Estimates and Log Rank Test
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Journal Article
Modeling Survival Data by Using Kaplan Meier Survival Estimates and Log Rank Test
2018
Overview
Survival analysis is generally defined as a set of statistical procedures for analyzing data for which the outcome variable of interest is time until an event occurs (Kartsonaki). In this study, we mean death. One of the popular options used to measure the fraction of subjects living for a certain amount of time after a specific treatment is Kaplan Meier estimates. It is one of the most frequently used nonparametric methods of modeling survival data (Kleinbaum). The main objective of this paper is to estimate the overall survival function for Acute Myeloid Leukemia Patients after Stem Cell Transplantation using the Kaplan Meier estimator and to obtain the survival estimates for each risk factor. And to compare the statistical differences between survival curves for two groups of subjects using Log Rank test. Survival tables, Kaplan Meier estimate curves and Log Rank tests were generated from the STATA software. It was found that probability of 1 year survival for AML patients after SCT was 54.58% and the probability of 2 years survival after SCT was 49.15% while the probability of 3 years survival after SCT was 47.46%.
Publisher
جامعة عين شمس - كلية التجارة
Subject
