Journal articles: 'Survival Data'

1

Healy, M. J. "Survival data." Archives of Disease in Childhood 73, no. 4 (October 1, 1995): 374–77. http://dx.doi.org/10.1136/adc.73.4.374.

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2

Jaisankar, R., and K. S. Parvatha Varshini. "On Addressing Censoring in Survival Data Using Fuzzy Theory." Indian Journal Of Science And Technology 17, no. 4 (January 26, 2024): 312–16. http://dx.doi.org/10.17485/ijst/v17i4.2288.

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3

Mattson, Mark P., and Steven W. Barger. "Silencing survival data." Trends in Neurosciences 23, no. 10 (October 2000): 466–67. http://dx.doi.org/10.1016/s0166-2236(00)01622-2.

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4

Fu, Wei, and Jeffrey S. Simonoff. "Survival trees for interval-censored survival data." Statistics in Medicine 36, no. 30 (August 18, 2017): 4831–42. http://dx.doi.org/10.1002/sim.7450.

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5

Pari Dayal L, Pari Dayal L., Leo Alexander T. Leo Alexander T, Ponnuraja C. Ponnuraja C, and Venkatesan P. Venkatesan P. "Modelling of Breast Cancer Survival Data: A Frailty Model Approach." Indian Journal of Applied Research 3, no. 10 (October 1, 2011): 1–3. http://dx.doi.org/10.15373/2249555x/oct2013/90.

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6

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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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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8

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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9

Gilks, Walter R., Sheila M. Gore, and Benjamin A. Bradley. "ANALYZING TRANSPLANT SURVIVAL DATA." Transplantation 42, no. 1 (July 1986): 46–49. http://dx.doi.org/10.1097/00007890-198607000-00009.

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10

Charra, B., J. M. Hurot, C. Chazot, C. VoVan, G. Jean, J. C. Terrat, T. Vanel, M. Ruffet, and G. Laurent. "Comparison of survival data." Kidney International 58, no. 2 (August 2000): 901. http://dx.doi.org/10.1046/j.1523-1755.2000.00244.x.

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11

Hougaard, Philip. "Fundamentals of Survival Data." Biometrics 55, no. 1 (March 1999): 13–22. http://dx.doi.org/10.1111/j.0006-341x.1999.00013.x.

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12

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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13

Crowther, Michael J., and Paul C. Lambert. "Simulating Complex Survival Data." Stata Journal: Promoting communications on statistics and Stata 12, no. 4 (December 2012): 674–87. http://dx.doi.org/10.1177/1536867x1201200407.

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14

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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15

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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16

Folger, William Ray. "Adrenalectomy: postoperative survival data." Journal of Feline Medicine and Surgery 18, no. 8 (June 2016): 683. http://dx.doi.org/10.1177/1098612x16652186.

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17

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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18

Sedgwick, P., and K. Joekes. "Survival (time to event) data: median survival times." BMJ 343, aug10 3 (August 10, 2011): d4890. http://dx.doi.org/10.1136/bmj.d4890.

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19

Jayakodi, G., N. Sundaram, and P. Venkatesan. "An Application of Exponential-Lindley Distribution in Modelling Cancer Survival Data." Indian Journal Of Science And Technology 15, no. 46 (December 12, 2022): 2579–88. http://dx.doi.org/10.17485/ijst/v15i46.1870.

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20

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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21

Hougaard, Philip. "Modelling heterogeneity in survival data." Journal of Applied Probability 28, no. 3 (September 1991): 695–701. http://dx.doi.org/10.2307/3214503.

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Ordinary survival models implicitly assume that all individuals in a group have the same risk of death. It may, however, be relevant to consider the group as heterogeneous, i.e. a mixture of individuals with different risks. For example, after an operation each individual may have constant hazard of death. If risk factors are not included, the group shows decreasing hazard. This offers two fundamentally different interpretations of the same data. For instance, Weibull distributions with shape parameter less than 1 can be generated as mixtures of constant individual hazards. In a proportional hazards model, neglect of a subset of the important covariates leads to biased estimates of the other regression coefficients. Different choices of distributions for the unobserved covariates are discussed, including binary, gamma, inverse Gaussian and positive stable distributions, which show both qualitative and quantitative differences. For instance, the heterogeneity distribution can be either identifiable or unidentifiable. Both mathematical and interpretational consequences of the choice of distribution are considered. Heterogeneity can be evaluated by the variance of the logarithm of the mixture distribution. Examples include occupational mortality, myocardial infarction and diabetes.

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22

Zhang, Zhongheng. "Statistical description for survival data." Annals of Translational Medicine 4, no. 20 (October 2016): 401. http://dx.doi.org/10.21037/atm.2016.07.17.

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23

Peng, Limin. "Quantile Regression for Survival Data." Annual Review of Statistics and Its Application 8, no. 1 (March 7, 2021): 413–37. http://dx.doi.org/10.1146/annurev-statistics-042720-020233.

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Quantile regression offers a useful alternative strategy for analyzing survival data. Compared with traditional survival analysis methods, quantile regression allows for comprehensive and flexible evaluations of covariate effects on a survival outcome of interest while providing simple physical interpretations on the time scale. Moreover, many quantile regression methods enjoy easy and stable computation. These appealing features make quantile regression a valuable practical tool for delivering in-depth analyses of survival data. This article provides a review of a comprehensive set of statistical methods for performing quantile regression with different types of survival data. The review covers various survival scenarios, including randomly censored data, data subject to left truncation or censoring, competing risks and semicompeting risks data, and recurrent events data. Two real-world examples are presented to illustrate the utility of quantile regression for practical survival data analyses.

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24

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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25

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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26

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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27

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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28

Žitnik, Marinka, and Blaž Zupan. "Survival regression by data fusion." Systems Biomedicine 2, no. 3 (July 3, 2014): 47–53. http://dx.doi.org/10.1080/21628130.2015.1016702.

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29

Hougaard, Philip. "Modelling heterogeneity in survival data." Journal of Applied Probability 28, no. 03 (September 1991): 695–701. http://dx.doi.org/10.1017/s0021900200042534.

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Abstract:

Ordinary survival models implicitly assume that all individuals in a group have the same risk of death. It may, however, be relevant to consider the group as heterogeneous, i.e. a mixture of individuals with different risks. For example, after an operation each individual may have constant hazard of death. If risk factors are not included, the group shows decreasing hazard. This offers two fundamentally different interpretations of the same data. For instance, Weibull distributions with shape parameter less than 1 can be generated as mixtures of constant individual hazards. In a proportional hazards model, neglect of a subset of the important covariates leads to biased estimates of the other regression coefficients. Different choices of distributions for the unobserved covariates are discussed, including binary, gamma, inverse Gaussian and positive stable distributions, which show both qualitative and quantitative differences. For instance, the heterogeneity distribution can be either identifiable or unidentifiable. Both mathematical and interpretational consequences of the choice of distribution are considered. Heterogeneity can be evaluated by the variance of the logarithm of the mixture distribution. Examples include occupational mortality, myocardial infarction and diabetes.

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30

Hougaard, Philip. "Frailty models for survival data." Lifetime Data Analysis 1, no. 3 (1995): 255–73. http://dx.doi.org/10.1007/bf00985760.

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31

Grégoire, G. "Survival Data and Regression Models." EAS Publications Series 66 (2014): 125–47. http://dx.doi.org/10.1051/eas/1466010.

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32

Samuelsen, Sven Ove, and Geir Egil Eide. "Attributable fractions with survival data." Statistics in Medicine 27, no. 9 (2008): 1447–67. http://dx.doi.org/10.1002/sim.3022.

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33

Atherton, Pamela J., Sumithra J. Mandrekar, and Jeff A. Sloan. "Combining Symptom and Survival Data." Current Problems in Cancer 30, no. 6 (November 2006): 307–18. http://dx.doi.org/10.1016/j.currproblcancer.2006.08.008.

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34

Cho, Youngjoo. "Regression discontinuity for survival data." Communications for Statistical Applications and Methods 31, no. 1 (January 31, 2024): 155–78. http://dx.doi.org/10.29220/csam.2024.31.1.155.

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35

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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36

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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37

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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38

Newell, J., J. W. Kay, and T. C. Aitchison. "Survival ratio plots with permutation envelopes in survival data problems." Computers in Biology and Medicine 36, no. 5 (May 2006): 526–41. http://dx.doi.org/10.1016/j.compbiomed.2005.03.005.

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39

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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40

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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41

Suantari, Ni Gusti Ayu Putu Puteri, Anwar Fitrianto, and Bagus Sartono. "COMPARATIVE STUDY OF SURVIVAL SUPPORT VECTOR MACHINE AND RANDOM SURVIVAL FOREST IN SURVIVAL DATA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 3 (September 30, 2023): 1495–502. http://dx.doi.org/10.30598/barekengvol17iss3pp1495-1502.

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Survival analysis is a statistical procedure in analyzing data with the response variable is time until an event occurs (time-to-event). In the last few years, many classification approaches have been developed in machine learning, but only a few considered the presence of time-to-event variable. Random Survival Forest and Survival Support Vector Machine are machine learning approach which is a nonparametric classification method when dealing with large data and a response variable of survival time. Random Survival Forest is tree based method that using boostrapping algorithm, and Survival Support Vector Machine using hybrid approaches between regression and ranking constrain. The data used in this study is generated data in the form of right-censored survival data. This study uses the RandomForestSRC and SurvivalSVM packages on R software. This study aimed to compare the performance of the Survival Support Vector Machine and Random Survival Forest methods using simulation studies. Simulation results on right-censored survival data using binary predictor variables scenario indicate that the Survival Support Vector Machine (SSVM) method with Radial Basic Function Kernel (RBF Kernel) has the best model performance on data with small volumes, whereas when the data volume becomes larger, the method that has the best performance is Survival Support Vector Machine using Additive Kernel. Meanwhile, Random Survival Forest is a method that has the best performance for all conditions in mixed predictor variables scenario. Method, proportion of censored data and size of data are factors that affect the model performance.

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42

Singh, R. S., and Xuewen Lu. "Nonparametric synthetic data regression estimation for censored survival data." Journal of Nonparametric Statistics 11, no. 1-3 (January 1999): 13–31. http://dx.doi.org/10.1080/10485259908832773.

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43

Hwang, Yi-Ting, Chia-Hui Huang, Chun-Chao Wang, Tzu-Yin Lin, and Yi-Kuan Tseng. "Joint modelling of longitudinal binary data and survival data." Journal of Applied Statistics 46, no. 13 (March 19, 2019): 2357–71. http://dx.doi.org/10.1080/02664763.2019.1590540.

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44

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. These are all necessary factors that will affect the survival of passengers. Through the corresponding code display of the open-source data set, this paper draws the corresponding conclusion and finds that the factors of passenger survival have a relatively large relationship and considerable impact on fare and berth level.

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45

Saadaoui, K., H. Sahli, S. Boussaid, S. Jemmali, S. Rekik, E. Cheour, and M. Elleuch. "AB0320 BDMARDS SURVIVAL: THE TUNISIAN DATA." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 1458.3–1459. http://dx.doi.org/10.1136/annrheumdis-2020-eular.6498.

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Background:The advent of biotherapies in the late 90s radically changed the face of inflammatory diseases including rheumatoid arthritis. The survival of these innovative therapies is an indicator, in clinical practice, of their long-term efficacy and safety.Objectives:The objective of this study was to assess their use in Tunisia through their survival during rheumatoid arthritis as well as to determine the factors that may influence their therapeutic maintenance in real life.Methods:This is a retrospective study including RA patients (ACR/EULAR 2010 criteria) and putted on biotherapy between 01-01-2014 and 12-31-2016. They were followed until 12-31-2018. The therapeutic maintenance rate at 12, 24, 36 and 48 months as well as the survival curves of biotherapies were analyzed using the Kaplan-Meier survival curves and compared by the Log-rank test. Reasons for interruption and patterns of biological change have been reported. Finally, an analysis of factors influencing survival was performed using Cox regression.A p<0.05 was considered statistically significant.Results:Three hundred seventy-four patients were included in the study; sex ratio was 0,147. The baseline age was 55 ± 12.5 years [20 – 90] and the average disease duration was 11.7 ± 6.7 years [2 – 41]. Rheumatoid factor and ACPA were positive respectively in 79% and 71% cases. After failure of cDMARD, the first biotherapy prescribed was etanercept in 54% of cases, adalimumab in 14% of cases, certolizumab pegol 13%, infliximab 6%, tocilizumab 6% and rituximab in 7% of cases, with an average DAS28 at baseline 6.01 ± 0.89 [5,37 – 6,50]. Association with methotrexate was observed in 59,6% case and with corticosteroid in 57.2% case. Drug persistency rate at 12 months was 85.8%; at 24 months, 69.9%; at 36 months, 60.6% and at 48 months, 55.9%. Survival was on average 41.7 months with 95% CI (39.47 - 43.91). The presence of rheumatoid factors, the co-prescription of methotrexate as well as good initial therapeutic response were predictor of a better survival of biologicals at a statistically significant level p<0.01 (Hazard Ratios for pursuit of biotherapy were respectively 1.79, 1.91 and 2,3). The use of glucocorticoids was a negative predictor of retention (Hazard Ratio for therapy pursuit was 0.47 p < 0.001). This first biotherapy was stopped in 39% of cases and ineffectiveness was the major reason of interruption (52.7%). The anti-TNFα cycling was the most adopted therapeutic strategy with 64.6% of cases. The survival rates of the second biotherapy at 12, 24 and 36 months were 91%, 76.4% and 72.1%, respectively.Conclusion:Our study provides information about biotherapy prescription practices in Tunisia and their effectiveness in “real life”. It informs us about the use of these new therapies in our country and has shown an efficacy and a tolerance profile close to those reported in international registers.Disclosure of Interests:None declared

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46

Ray, W. D., and D. Collett. "Modelling Survival Data in Medical Research." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 1 (1995): 188. http://dx.doi.org/10.2307/2983419.

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47

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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48

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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49

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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50

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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Journal articles on the topic 'Survival Data'

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