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Poisson-Exponential Distribution Approach to Survival Analysis of Right-Censored Data of Lung Cancer Patients

Jurnal Matematika, Statistika dan Komputasi · Vol. 22 No. 2 (2026) · hlm. 301–314

DOI
10.20956/j.v22i2.46746
ISSN
2614-8811, 1858-1382
Terbit
2026-01-10
Penerbit
Department of Mathematics, Hasanuddin University
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Survival analysis of lung cancer patients is essential for understanding the dynamics of their survival probabilities over time. The Poisson–Exponential (PE) distribution is particularly relevant for data involving complementary risks and right-censoring, and it provides a framework for comparing different parameter estimation approaches. This study aims to estimate the parameters of the PE distribution for the survival times of lung cancer patients treated at Dr. Wahidin Sudirohusodo Hospital, Makassar, in 2015, and to compare the performance of two estimation methods: Maximum Likelihood Estimation (MLE) and Maximum Product Spacing (MPS). Parameter estimation was conducted numerically using the Newton–Raphson algorithm, resulting in and. The survival probabilities decline as survival time increases, and both the PE–MLE and PE–MPS curves closely follow the Kaplan–Meier estimator. All information criteria indicate that MPS outperforms MLE, as reflected by a higher log-likelihood and lower AIC, AICc, and CAIC values. These findings demonstrate that the PE distribution is suitable for modeling the survival dynamics of lung cancer patients, with MPS identified as the most appropriate parameter estimation method for the analyzed dataset.

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  • Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi
  • Institut Agama Islam Negeri Kendari
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Siswanto SiswantoAfiliasi tidak diketahui4
Erna Tri HerdianiAfiliasi tidak diketahui3
Andi M Alfin BasoAfiliasi tidak diketahui3
Georgina M. TinungkiAfiliasi tidak diketahui2
Andi Kresna JayaAfiliasi tidak diketahui2
- SiswantoAfiliasi tidak diketahui2
Nirwan IlyasAfiliasi tidak diketahui2
Ayu Pratika PutriAfiliasi tidak diketahui2
Annurial Fitrayah TaufiqAfiliasi tidak diketahui2
Ahmad Maulana SyarifAfiliasi tidak diketahui2
Nurmimie KharinaAfiliasi tidak diketahui2
Samsir Aditya AniaAfiliasi tidak diketahui2
Nurfadilah NurfadilahInstitut Agama Islam Negeri Kendari2
Muhammad Ridzky DavalaAfiliasi tidak diketahui2
Raupong RaupongAfiliasi tidak diketahui1
Vieri KoerniawanAfiliasi tidak diketahui1
Georgina Maria TinungkiAfiliasi tidak diketahui1
Anna IslamiyatiAfiliasi tidak diketahui1
Novilia JaoAfiliasi tidak diketahui1
Riska RasyidAfiliasi tidak diketahui1
Ika Pratiwi HayaAfiliasi tidak diketahui1
Sitti SahrimanAfiliasi tidak diketahui1
Muhammad FadilAfiliasi tidak diketahui1
A.Muthiah Nur AngrianyAfiliasi tidak diketahui1
Rini JamalAfiliasi tidak diketahui1
Andi FebriyantiAfiliasi tidak diketahui1
Rahmiati RahimAfiliasi tidak diketahui1
Herlina RasyidUniversitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi1
Nur Aliah RamadhaniAfiliasi tidak diketahui1
Nadia Nazwadiah CaesarAfiliasi tidak diketahui1
Zaraswati Dwyana ZainuddinAfiliasi tidak diketahui1
Agung Muhammad TakdirAfiliasi tidak diketahui1
Nasrah SirajangAfiliasi tidak diketahui1
YULIANA.DEWI PutriAfiliasi tidak diketahui1
Puji Puspa SariAfiliasi tidak diketahui1
Nur Aminah AhmadAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 5 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Poisson-Exponential Distribution Approach to Survival Analysis of Right-Censored Data of Lung Cancer Patients</dc:title>
	<dc:creator xml:lang="en">Yunita, Andi Isna</dc:creator>
	<dc:creator xml:lang="en">Sunusi, Nurtiti</dc:creator>
	<dc:subject xml:lang="en">analisis survival</dc:subject>
	<dc:subject xml:lang="en">data tersensor kanan</dc:subject>
	<dc:subject xml:lang="en">poisson-exponential</dc:subject>
	<dc:subject xml:lang="en">MLE</dc:subject>
	<dc:subject xml:lang="en">MPS</dc:subject>
	<dc:description xml:lang="en">Survival analysis of lung cancer patients is essential for understanding the dynamics of their survival probabilities over time. The Poisson–Exponential (PE) distribution is particularly relevant for data involving complementary risks and right-censoring, and it provides a framework for comparing different parameter estimation approaches. This study aims to estimate the parameters of the PE distribution for the survival times of lung cancer patients treated at Dr. Wahidin Sudirohusodo Hospital, Makassar, in 2015, and to compare the performance of two estimation methods: Maximum Likelihood Estimation (MLE) and Maximum Product Spacing (MPS). Parameter estimation was conducted numerically using the Newton–Raphson algorithm, resulting in  and . The survival probabilities decline as survival time increases, and both the PE–MLE and PE–MPS curves closely follow the Kaplan–Meier estimator. All information criteria indicate that MPS outperforms MLE, as reflected by a higher log-likelihood and lower AIC, AICc, and CAIC values. These findings demonstrate that the PE distribution is suitable for modeling the survival dynamics of lung cancer patients, with MPS identified as the most appropriate parameter estimation method for the analyzed dataset.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2026-01-10</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/46746</dc:identifier>
	<dc:identifier>10.20956/j.v22i2.46746</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 22 No. 2 (2026): JANUARY 2026; 301-314</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/46746/13978</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>