Analisis Survival Data Kejadian Bersama dengan Pendekatan Efron Partial Likelihood

(Studi Kasus: Lama Masa Studi Mahasiswa Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Angkatan 2011)

  • Santi Prabawati Laboratorium Statistika Terapan Jurusan Matematika FMIPA Universitas Mulawarman
  • Yuki Novia Nasution Laboratorium Matematika Komputasi Jurusan Matematika FMIPA Universitas Mulawarman
  • Sri Wahyuningsih Laboratorium Statistika Terapan Jurusan Matematika FMIPA Universitas Mulawarman

Abstract

Survival analysis is a statistical procedure used to analyze data related to survival time, from the defined time origin until the occurrence of certain events. In the survival analysis, sometimes ties are found, in which two or more individuals experience the same event at the same time. There are three widely used methods to treat ties in survival analysis, that is Exact method, Breslow approach, and Efron approach. Efron's approach has a simple, fast, and accurate calculation especially when the data contains many ties. The purpose of this study is to find out the Cox proportional hazard data ties model using Efron partial likelihood approach and to know the variables that affect the graduation time of student of Faculty of Mathematics and Natural Sciences of Mulawarman University class of 2011 that graduated until February 28, 2017. The variables are Gender, home area, funding sources, and GPA. Based on the results of the analysis that has been done with the help of software R, it is obtained that the variables that have significant effect are gender and GPA. For the gender variables it was concluded that female students had a chance of 1,362 times to graduate faster than male students. While for the GPA variable it is concluded that each addition of GPA of 0.1, then the student's chance to graduate faster will increase by 1,225 times.

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Published
2018-11-09
How to Cite
PRABAWATI, Santi; NASUTION, Yuki Novia; WAHYUNINGSIH, Sri. Analisis Survival Data Kejadian Bersama dengan Pendekatan Efron Partial Likelihood. EKSPONENSIAL, [S.l.], v. 9, n. 1, p. 75-84, nov. 2018. ISSN 2798-3455. Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/278>. Date accessed: 02 may 2024.
Section
Articles