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Articles de revues sur le sujet « Survival data analysi »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

Tsaniya, Ulya, Triastuti Wuryandari, and Dwi Ispriyanti. "ANALISIS SURVIVAL PADA DATA KEJADIAN BERULANG MENGGUNAKAN PENDEKATAN COUNTING PROCESS." Jurnal Gaussian 11, no. 3 (2022): 377–85. http://dx.doi.org/10.14710/j.gauss.11.3.377-385.

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Asthma is a disorder that attacks the respiratory tract and causes bronchial hyperactivity to various stimuli characterized by recurrent episodic symptoms such as wheezing, coughing, shortness of breath, and heaviness in the chest. Asthma sufferers will experience exacerbations, namely episodes of asthma recurrence which gradually worsens progressively accompanied by the same symptoms. The length of time a person experiences an exacerbation can be influenced by various factors. To analyze this, the Cox regression model can be used which is within the scope of survival analysis where time is th

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2

N.Sundaram, N. Sundaram, and P. Venkatesan P.Venkatesan. "Modeling of Parametric Bayesian Cure Rate Survival for Pulmonary Tuberculosis Data Analysis." International Journal of Scientific Research 3, no. 6 (2012): 35–49. http://dx.doi.org/10.15373/22778179/june2014/171.

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3

V. Vallinayagam, V. Vallinayagam, S. Parthasarathy S. Parthasarathy, and P. Venkatesan P. Venkatesan. "A Comparative Study of Life Time Models in the Analysis of Survival Data." Indian Journal of Applied Research 4, no. 1 (2011): 344–47. http://dx.doi.org/10.15373/2249555x/jan2014/101.

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4

Coa Clemente, Ramiro. "¿CUÁL ES EL MODELO MÁS APROPIADO PARA ANALIZAR DATOS DE SOBREVIVENCIA INFANTIL EN BOLIVIA?" Varianza 21, no. 21 (2023): 1–13. http://dx.doi.org/10.53287/skjt1649td12r.

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El objetivo principal de este estudio es determinar cuál es el modelo más apropiado para analizar datos de sobrevivencia infantil en Bolivia. Además del modelo semiparamétrico de riesgos proporcionales de Cox, se evalúan tres modelos paramétricos: exponencial, Weibull y gamma generalizado. Para abordar el objetivo se comparan tres resultados: los coeficientes estimados a partir de los distintos modelos, la significancia de las covariables en términos del valor-p y el criterio de información de Akaike (AIC). Se concluye que el modelo Weibull es más apropiado que el modelo de Cox para analizar d

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5

Vissani, Francesco. "Joint analysis of Borexino and SNO solar neutrino data and reconstruction of the survival probability." Nuclear Physics and Atomic Energy 18, no. 4 (2017): 303–12. http://dx.doi.org/10.15407/jnpae2017.04.303.

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6

Asakura, Koko, and Toshimitsu Hamasaki. "Analysis of survival data." Drug Delivery System 30, no. 5 (2015): 474–84. http://dx.doi.org/10.2745/dds.30.474.

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7

Breslow, N., D. R. Cox, and D. Oakes. "Analysis of Survival Data." Biometrics 41, no. 2 (1985): 593. http://dx.doi.org/10.2307/2530888.

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8

Jayet, H., and A. Moreau. "Analysis of survival data." Journal of Econometrics 48, no. 1-2 (1991): 263–85. http://dx.doi.org/10.1016/0304-4076(91)90041-b.

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9

Lagakos, S. "Analysis of survival data." Controlled Clinical Trials 7, no. 1 (1986): 85. http://dx.doi.org/10.1016/0197-2456(86)90009-7.

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10

Schoenfeld, David, D. R. Cox, and D. Oakes. "Analysis of Survival Data." Journal of the American Statistical Association 81, no. 394 (1986): 572. http://dx.doi.org/10.2307/2289259.

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11

Kenyon, James R. "Analysis of Multivariate Survival Data." Technometrics 44, no. 1 (2002): 86–87. http://dx.doi.org/10.1198/tech.2002.s658.

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12

Yan, Jun. "Analysis of Multivariate Survival Data." Journal of the American Statistical Association 100, no. 469 (2005): 354–55. http://dx.doi.org/10.1198/jasa.2005.s10.

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13

Emura, Takeshi, Takahiro Hasegawa, Masataka Taguri, and Masahiko Gosho. "Special Section : Survival Data Analysis." Japanese Journal of Biometrics 45, no. 2 (2024): 133. http://dx.doi.org/10.5691/jjb.45.133.

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14

Tilling, Kate. "Analysis of Multivariate Survival Data." International Journal of Epidemiology 30, no. 4 (2001): 909–10. http://dx.doi.org/10.1093/ije/30.4.909.

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15

Gupta, Ramesh C., Nandini Kannan, and Aparna Raychaudhuri. "Analysis of lognormal survival data." Mathematical Biosciences 139, no. 2 (1997): 103–15. http://dx.doi.org/10.1016/s0025-5564(96)00133-2.

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16

Chan, K. C. G. "Survival analysis without survival data: connecting length-biased and case-control data." Biometrika 100, no. 3 (2013): 764–70. http://dx.doi.org/10.1093/biomet/ast008.

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17

Messori, Andrea, Sabrina Trippoli, Monica Vaiani, and Francesco Cattel. "Survival Meta-Analysis of Individual Patient Data and Survival Meta-Analysis of Published (Aggregate) Data." Clinical Drug Investigation 20, no. 5 (2000): 309–16. http://dx.doi.org/10.2165/00044011-200020050-00002.

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18

El-Bayomi, Kh M., El A. Rady, M. S. El-Tarabany, and Fatma D. Mohammed. "Statistical Analysis of Biological Survival Data." Zagazig Veterinary Journal 42, no. 1 (2014): 129–39. http://dx.doi.org/10.21608/zvjz.2014.59478.

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19

Lumley, Thomas, and Patrick Heagerty. "Graphical Exploratory Analysis of Survival Data." Journal of Computational and Graphical Statistics 9, no. 4 (2000): 738. http://dx.doi.org/10.2307/1391090.

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20

Lachenbruch, Peter A., and Elisa T. Lee. "Statistical Methods for Survival Data Analysis." Journal of the American Statistical Association 88, no. 421 (1993): 380. http://dx.doi.org/10.2307/2290742.

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21

Elton, R. A., and E. T. Lee. "Statistical Methods for Survival Data Analysis." Biometrics 51, no. 1 (1995): 383. http://dx.doi.org/10.2307/2533355.

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22

Noe, D. A., and W. R. Bell. "ANALYSIS OF RED CELL SURVIVAL DATA." British Journal of Haematology 63, no. 2 (2008): 398–400. http://dx.doi.org/10.1111/j.1365-2141.1986.tb05568.x.

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23

Dornhorst, A. C., Dennis A. Noe, and William R. Bell. "ANALYSIS OF RED CELL SURVIVAL DATA." British Journal of Haematology 65, no. 1 (1987): 117. http://dx.doi.org/10.1111/j.1365-2141.1987.tb06145.x.

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24

Finkelstein, Dianne M. "Analysis of Failure and Survival Data." Technometrics 44, no. 4 (2002): 397. http://dx.doi.org/10.1198/tech.2002.s74.

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25

Betensky, Rebecca A. "Analysis of Failure and Survival Data." Journal of the American Statistical Association 98, no. 463 (2003): 771–72. http://dx.doi.org/10.1198/jasa.2003.s295.

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26

VanderWeele, Tyler J. "Causal Mediation Analysis With Survival Data." Epidemiology 22, no. 4 (2011): 582–85. http://dx.doi.org/10.1097/ede.0b013e31821db37e.

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27

Lee, Elisa T. "Statistical Methods for Survival Data Analysis." IEEE Transactions on Reliability 35, no. 1 (1986): 123. http://dx.doi.org/10.1109/tr.1986.4335370.

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28

Dutz, Almut, and Steffen Löck. "Competing risks in survival data analysis." Radiotherapy and Oncology 130 (January 2019): 185–89. http://dx.doi.org/10.1016/j.radonc.2018.09.007.

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29

Lumley, Thomas, and Patrick Heagerty. "Graphical Exploratory Analysis of Survival Data." Journal of Computational and Graphical Statistics 9, no. 4 (2000): 738–49. http://dx.doi.org/10.1080/10618600.2000.10474910.

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30

Ziegel, Eric R. "Statistical Methods for Survival Data Analysis." Technometrics 35, no. 1 (1993): 101. http://dx.doi.org/10.1080/00401706.1993.10485024.

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31

Heller, Glenn. "Analysis of Failure and Survival Data:." Controlled Clinical Trials 24, no. 3 (2003): 353–54. http://dx.doi.org/10.1016/s0197-2456(02)00342-2.

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32

Saggu, P. S., and E. T. Lee. "Statistical Methods for Survival Data Analysis." Statistician 43, no. 4 (1994): 607. http://dx.doi.org/10.2307/2348154.

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33

Bodian, Carol A., and Elisa T. Lee. "Statistical Methods for Survival Data Analysis." Journal of the American Statistical Association 80, no. 392 (1985): 1080. http://dx.doi.org/10.2307/2288605.

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34

Gillespie, Brenda W. "Statistical Methods for Survival Data Analysis." International Statistical Review 83, no. 1 (2015): 167–68. http://dx.doi.org/10.1111/insr.12095_7.

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35

Sinha, Debajyoti, and Dipak K. Dey. "Semiparametric Bayesian Analysis of Survival Data." Journal of the American Statistical Association 92, no. 439 (1997): 1195–212. http://dx.doi.org/10.1080/01621459.1997.10474077.

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36

Simon, Richard, and Richard Shaker. "Interactive statistical analysis of survival data." Computers and Biomedical Research 20, no. 1 (1987): 49–54. http://dx.doi.org/10.1016/0010-4809(87)90017-6.

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37

Lin, D. Y. "Survival analysis with incomplete genetic data." Lifetime Data Analysis 20, no. 1 (2013): 16–22. http://dx.doi.org/10.1007/s10985-013-9262-8.

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38

Gao, Linghan. "Data set analysis of Titanic distress data." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 323–29. http://dx.doi.org/10.54097/whp21y56.

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The main purpose of this paper is to study the sinking of Titanic, and the Titanic data set, which is open source on kaggle, is the background support resource for this research. This paper makes use of random Forest and Cox proportional risk models as well as survival and cumulative risk functions, which have been carefully calibrated and calibrated accordingly, so as to analyze in detail the factors affecting the survival of passengers on Titanic and what allowed them to survive. It's the class of shipping space or the port of departure or the family and friends you're bringing with you. The

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39

Yamamoto, Dr Kenji, and Prof Lijuan Wang. "LEVERAGING DEEP LEARNING IN SURVIVAL ANALYSIS FOR ENHANCED TIME-TO-EVENT PREDICTION." International Journal of Advanced Artificial Intelligence Research 2, no. 05 (2025): 1–6. https://doi.org/10.55640/ijaair-v02i05-01.

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Survival analysis is a critical statistical approach for modeling time-to-event outcomes across disciplines such as healthcare, engineering, and social sciences. Traditional methods, including Cox proportional hazards models, often struggle with complex, high-dimensional data and non-linear relationships. Recent advancements in deep learning have led to innovative models that significantly enhance survival prediction by capturing intricate patterns and dependencies in time-to-event data. This study explores state-of-the-art deep learning frameworks—such as DeepSurv, DeepHit, and recurrent neur

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40

SHAMILOV, Aladdin, and Sevda ÖZDEMİR. "Survival Data Analysis by Minminxent and Maxminxent Methods." Turkiye Klinikleri Journal of Biostatistics 9, no. 1 (2017): 23–34. http://dx.doi.org/10.5336/biostatic.2016-52392.

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41

In, Junyong, and Dong Kyu Lee. "Survival analysis: part II – applied clinical data analysis." Korean Journal of Anesthesiology 72, no. 5 (2019): 441–57. http://dx.doi.org/10.4097/kja.19183.

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42

Hao, Lin, Juncheol Kim, Sookhee Kwon, and Il Do Ha. "Deep Learning-Based Survival Analysis for High-Dimensional Survival Data." Mathematics 9, no. 11 (2021): 1244. http://dx.doi.org/10.3390/math9111244.

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With the development of high-throughput technologies, more and more high-dimensional or ultra-high-dimensional genomic data are being generated. Therefore, effectively analyzing such data has become a significant challenge. Machine learning (ML) algorithms have been widely applied for modeling nonlinear and complicated interactions in a variety of practical fields such as high-dimensional survival data. Recently, multilayer deep neural network (DNN) models have made remarkable achievements. Thus, a Cox-based DNN prediction survival model (DNNSurv model), which was built with Keras and TensorFl

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43

Vickers, A. D. "SURVIVAL NETWORK META-ANALYSIS: HAZARD RATIOS VERSUS RECONSTRUCTED SURVIVAL DATA." Value in Health 19, no. 3 (2016): A90. http://dx.doi.org/10.1016/j.jval.2016.03.1820.

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44

Bagdonavicius, V. "Analysis of survival data with cross-effects of survival functions." Biostatistics 5, no. 3 (2004): 415–25. http://dx.doi.org/10.1093/biostatistics/kxg045.

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45

Mukaromah, Muizzatul. "Analisis Survival pada Data Kanker Ovarium." MATHunesa: Jurnal Ilmiah Matematika 8, no. 2 (2020): 130–34. http://dx.doi.org/10.26740/mathunesa.v8n2.p130-134.

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Penyakit kanker ovarium merupakan penyakit yang mematikan bagi wanita. Hingga saat ini pasien kanker ovarium terus meningkat, dikarenakan penyakit ini didiagnosa pada stadium akhir yaitu pada staium 3 dan 4. Mengingat fakta yang ada di masyarakat,maka perlu adanya analisis mengenai pasien kanker ovarium. Sehingga dapat diketahui faktor-faktor yang mempengaruhi kesembuhan pasien kanker ovarium. Regresi Weibull merupakan metode analisis survival yang digunakan untuk mengetahui efek variabel independen dengan data survival sebagai variabel dependen. Dalam penelitian ini akan mengkaji model data s

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46

Johnson, Wesley, and Ronald Christensen. "Bayesian nonparametric survival analysis for grouped data." Canadian Journal of Statistics 14, no. 4 (1986): 307–14. http://dx.doi.org/10.2307/3315188.

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47

Guo, S. W., and D. Y. Lin. "Regression Analysis of Multivariate Grouped Survival Data." Biometrics 50, no. 3 (1994): 632. http://dx.doi.org/10.2307/2532778.

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48

Stepanova, Maria, and Lyn Thomas. "Survival Analysis Methods for Personal Loan Data." Operations Research 50, no. 2 (2002): 277–89. http://dx.doi.org/10.1287/opre.50.2.277.426.

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49

Egli, Philipp, and Bernhard Schmid. "The analysis of complex leaf survival data." Basic and Applied Ecology 2, no. 3 (2001): 223–31. http://dx.doi.org/10.1078/1439-1791-00048.

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50

Masarifoglu, Melik, and Ali Hakan Buyuklu. "Applying Survival Analysis to Telecom Churn Data." American Journal of Theoretical and Applied Statistics 8, no. 6 (2019): 261. http://dx.doi.org/10.11648/j.ajtas.20190806.18.

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