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Journal articles on the topic 'Survival data analysi'

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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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 (August 28, 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 the dependent variable. In the survival analysis, asthma exacerbations were identical/recurrent events where the individual experienced the event more than once during the study. If the survival data contains identical/recurrent events, the analysis uses a counting process approach. Counting Process is an approach used to deal with survival data with identical recurrent events, meaning that recurrences are caused by the same thing, which in this case is the narrowing of the bronchioles in asthmatics. The purpose of this study was to determine the factors that cause asthma exacerbations by using a counting process approach as a data treatment for recurrent events at Diponegoro National Hospital. Based on the results of the analysis, the factors that influence the length of time a patient experiences an exacerbation are the age, gender, and type of cases

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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 (June 1, 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 (October 1, 2011): 344–47. http://dx.doi.org/10.15373/2249555x/jan2014/101.

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4

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 (December 25, 2017): 303–12. http://dx.doi.org/10.15407/jnpae2017.04.303.

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5

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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6

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

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7

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

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8

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

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9

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

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10

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

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11

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

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12

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

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13

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

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14

Chan, K. C. G. "Survival analysis without survival data: connecting length-biased and case-control data." Biometrika 100, no. 3 (April 7, 2013): 764–70. http://dx.doi.org/10.1093/biomet/ast008.

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15

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 (November 2000): 309–16. http://dx.doi.org/10.2165/00044011-200020050-00002.

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16

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 (March 1, 2014): 129–39. http://dx.doi.org/10.21608/zvjz.2014.59478.

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17

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

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18

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

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19

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

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20

Noe, D. A., and W. R. Bell. "ANALYSIS OF RED CELL SURVIVAL DATA." British Journal of Haematology 63, no. 2 (March 12, 2008): 398–400. http://dx.doi.org/10.1111/j.1365-2141.1986.tb05568.x.

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21

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

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22

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

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23

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

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24

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

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25

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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26

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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27

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

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28

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

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29

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

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30

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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31

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

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32

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

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33

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

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34

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

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35

Lin, D. Y. "Survival analysis with incomplete genetic data." Lifetime Data Analysis 20, no. 1 (May 31, 2013): 16–22. http://dx.doi.org/10.1007/s10985-013-9262-8.

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36

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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37

Wuryandari, Triastuti, Sri Haryatmi Kartiko, and Danardono Danardono. "ANALISIS SURVIVAL UNTUK DURASI PROSES KELAHIRAN MENGGUNAKAN MODEL REGRESI HAZARD ADDITIF." Jurnal Gaussian 9, no. 4 (December 7, 2020): 402–10. http://dx.doi.org/10.14710/j.gauss.v9i4.29259.

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Survival data is the length of time until an event occurs. If the survival time is affected by other factor, it can be modeled with a regression model. The regression model for survival data is commonly based on the Cox proportional hazard model. In the Cox proportional hazard model, the covariate effect act multiplicatively on unknown baseline hazard. Alternative to the multiplicative hazard model is the additive hazard model. One of the additive hazard models is the semiparametric additive hazard model that introduced by Lin Ying in 1994. The regression coefficient estimates in this model mimic the scoring equation in the Cox model. Score equation of Cox model is the derivative of the Partial Likelihood and methods to maximize partial likelihood with Newton Raphson iterasi. Subject from this paper is describe the multiplicative and additive hazard model that applied to the duration of the birth process. The data is obtained from two different clinics,there are clinic that applies gentlebirth method while the other one no gentlebirth. From the data processing obtained the factors that affect on the duration of the birth process are baby’s weight, baby’s height and method of birth. Keywords: survival, additive hazard model, cox proportional hazard, partial likelihood, gentlebirth, duration

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38

In, Junyong, and Dong Kyu Lee. "Survival analysis: part II – applied clinical data analysis." Korean Journal of Anesthesiology 72, no. 5 (October 1, 2019): 441–57. http://dx.doi.org/10.4097/kja.19183.

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39

Hao, Lin, Juncheol Kim, Sookhee Kwon, and Il Do Ha. "Deep Learning-Based Survival Analysis for High-Dimensional Survival Data." Mathematics 9, no. 11 (May 28, 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 TensorFlow, was developed. However, its results were only evaluated on the survival datasets with high-dimensional or large sample sizes. In this paper, we evaluated the prediction performance of the DNNSurv model using ultra-high-dimensional and high-dimensional survival datasets and compared it with three popular ML survival prediction models (i.e., random survival forest and the Cox-based LASSO and Ridge models). For this purpose, we also present the optimal setting of several hyperparameters, including the selection of a tuning parameter. The proposed method demonstrated via data analysis that the DNNSurv model performed well overall as compared with the ML models, in terms of the three main evaluation measures (i.e., concordance index, time-dependent Brier score, and the time-dependent AUC) for survival prediction performance.

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40

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

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41

Bagdonavicius, V. "Analysis of survival data with cross-effects of survival functions." Biostatistics 5, no. 3 (July 1, 2004): 415–25. http://dx.doi.org/10.1093/biostatistics/kxg045.

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42

Mukaromah, Muizzatul. "Analisis Survival pada Data Kanker Ovarium." MATHunesa: Jurnal Ilmiah Matematika 8, no. 2 (June 26, 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 survival pada pasien kanker ovarium dan mengetahui faktor yang mempengaruhi kesembuhan pasien kanker ovarium. Variabel yang digunakan yaitu riwayat pengobatan, usia pasien, dan alat kontrasepsi. Sehingga menghasilkan variabel riwayat pengobatan dan usia pasien yang diduga mempengaruhi kesembuhan kanker ovarium. Setiap pasien kanker ovarium yang melakukan pengobatan dalam riwayat pengobatan mempunyai kemungkinan untuk sembuh 4,2503 kali dan setiap bertambahnya usia pasien mempunyai kemungkinan sembuh 0,1107 kali.

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43

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

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44

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

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45

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

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46

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

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47

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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48

Ghosh, Debashis. "Book Review: Analysis of multivariate survival data." Statistical Methods in Medical Research 10, no. 4 (August 2001): 306–7. http://dx.doi.org/10.1177/096228020101000407.

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49

Reeves, G. K., and S. E. Overton. "PRELIMINARY SURVIVAL ANALYSIS OF UK AIDS DATA." Lancet 331, no. 8590 (April 1988): 880. http://dx.doi.org/10.1016/s0140-6736(88)91622-4.

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50

SWEETING, TREVOR J. "Approximate Bayesian analysis of censored survival data." Biometrika 74, no. 4 (1987): 809–16. http://dx.doi.org/10.1093/biomet/74.4.809.

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