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Journal articles on the topic 'Survival probability'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Zarate-Herrada, David A., Lea F. Santos, and E. Jonathan Torres-Herrera. "Generalized Survival Probability." Entropy 25, no. 2 (2023): 205. http://dx.doi.org/10.3390/e25020205.

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Survival probability measures the probability that a system taken out of equilibrium has not yet transitioned from its initial state. Inspired by the generalized entropies used to analyze nonergodic states, we introduce a generalized version of the survival probability and discuss how it can assist in studies of the structure of eigenstates and ergodicity.

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2

Casati, Giulio, Italo Guarneri, and Giulio Maspero. "Fractal Survival Probability Fluctuations." Physical Review Letters 84, no. 1 (2000): 63–66. http://dx.doi.org/10.1103/physrevlett.84.63.

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3

Wadia, Reena. "Survival probability of autotransplanted teeth." British Dental Journal 226, no. 12 (2019): 950. http://dx.doi.org/10.1038/s41415-019-0466-5.

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4

Crawford, T. O. "Survival probability in ataxia telangiectasia." Archives of Disease in Childhood 91, no. 7 (2005): 610–11. http://dx.doi.org/10.1136/adc.2006.094268.

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5

Stockman, J. A. "Survival probability in ataxia telangiectasia." Yearbook of Pediatrics 2008 (January 2008): 364–66. http://dx.doi.org/10.1016/s0084-3954(08)70471-8.

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6

Luo, Jian-hua. "Survival probability and ruin probability of a risk model." Applied Mathematics-A Journal of Chinese Universities 23, no. 3 (2008): 256–64. http://dx.doi.org/10.1007/s11766-008-1916-z.

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7

Hanada, Toshiya, Hiroshi Hirayama, Atsushi Oishi, Yosuke Tanaka, and Tetsuo Yasaka. "Survival Probability Assessment of Space Tethers." JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 54, no. 630 (2006): 295–304. http://dx.doi.org/10.2322/jjsass.54.295.

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8

García-Calderón, Gastón, Verónica Riquer, and Roberto Romo. "Survival probability of a single resonance." Journal of Physics A: Mathematical and General 34, no. 19 (2001): 4155–65. http://dx.doi.org/10.1088/0305-4470/34/19/313.

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9

Levin, E., A. D. Martin, and M. G. Ryskin. "Survival probability of large rapidity gaps." Journal of Physics G: Nuclear and Particle Physics 25, no. 7 (1999): 1507–10. http://dx.doi.org/10.1088/0954-3899/25/7/336.

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10

Dettmann, Carl P., and Mohammed R. Rahman. "Survival probability for open spherical billiards." Chaos: An Interdisciplinary Journal of Nonlinear Science 24, no. 4 (2014): 043130. http://dx.doi.org/10.1063/1.4900776.

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11

Biau, David Jean, Aur??lien Latouche, and Rapha??l Porcher. "Competing Events Influence Estimated Survival Probability." Clinical Orthopaedics and Related Research 462 (September 2007): 229–33. http://dx.doi.org/10.1097/blo.0b013e3180986753.

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12

Gotsman, E. "Survival probability in hadron-hadron interactions." Nuclear Physics B - Proceedings Supplements 79, no. 1-3 (1999): 389–92. http://dx.doi.org/10.1016/s0920-5632(99)00732-x.

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13

Dettmann, Carl P., and Orestis Georgiou. "Survival probability for the stadium billiard." Physica D: Nonlinear Phenomena 238, no. 23-24 (2009): 2395–403. http://dx.doi.org/10.1016/j.physd.2009.09.019.

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14

Luo, Shunlong. "On survival probability of quantum states." Journal of Physics A: Mathematical and General 38, no. 13 (2005): 2991–95. http://dx.doi.org/10.1088/0305-4470/38/13/012.

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15

Gajek, Lesław, and Dariusz Zagrodny. "Reinsurance Arrangements Maximizing Insurer's Survival Probability." Journal of Risk and Insurance 71, no. 3 (2004): 421–35. http://dx.doi.org/10.1111/j.0022-4367.2004.00097.x.

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16

Yang, Grace L. "Cell survival probability under ionizing radiation." Mathematical Biosciences 112, no. 2 (1992): 305–17. http://dx.doi.org/10.1016/0025-5564(92)90029-v.

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17

Muga, J. G., G. W. Wei, and R. F. Snider. "Survival Probability for the Yamaguchi Potential." Annals of Physics 252, no. 2 (1996): 336–56. http://dx.doi.org/10.1006/aphy.1996.0135.

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18

Hemyari, Parichehr. "ROBUSTNESS OF THE QUARTILES OF SURVIVAL TIME AND SURVIVAL PROBABILITY." Journal of Biopharmaceutical Statistics 10, no. 3 (2000): 299–318. http://dx.doi.org/10.1081/bip-100102496.

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19

Buci, Skender, and Agim Kukeli. "Survival probability in patients with liver trauma." International Journal of Health Care Quality Assurance 29, no. 7 (2016): 778–85. http://dx.doi.org/10.1108/ijhcqa-04-2016-0045.

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Purpose – The purpose of this paper is to assess the survival probability among patients with liver trauma injury using the anatomical and psychological scores of conditions, characteristics and treatment modes. Design/methodology/approach – A logistic model is used to estimate 173 patients’ survival probability. Data are taken from patient records. Only emergency room patients admitted to University Hospital of Trauma (former Military Hospital) in Tirana are included. Data are recorded anonymously, preserving the patients’ privacy. Findings – When correctly predicted, the logistic models show

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20

Palmer, Duncan, and Niel Krige. "The financial survival probability of living annuitants." Journal of Economic and Financial Sciences 6, no. 1 (2013): 167–78. http://dx.doi.org/10.4102/jef.v6i1.282.

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This study addresses the question of how long a given amount of capital will be able to fund a living annuitant if the following five parameters are known: expected retirement duration (i.e. years between date of retirement and date of death), return on investment, inflation, annual withdrawal amount and initial capital amount available. A model (the Pension Model) that graphically depicts the relationship between these parameters was developed. This model facilitates retirement planning by showing how retirement duration and withdrawal rates change the financial “Survival Probability” (SP), w

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21

Pietronero, L. "Survival Probability for Kinetic Self-Avoiding Walks." Physical Review Letters 55, no. 19 (1985): 2025–27. http://dx.doi.org/10.1103/physrevlett.55.2025.

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22

Bhat, C. M., P. C. Bhat, M. Paterno, and H. B. Prosper. "Study of the Solar Neutrino Survival Probability." Physical Review Letters 81, no. 23 (1998): 5056–59. http://dx.doi.org/10.1103/physrevlett.81.5056.

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23

Redner, S. "Survival probability in a random velocity field." Physical Review E 56, no. 5 (1997): 4967–72. http://dx.doi.org/10.1103/physreve.56.4967.

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24

Poltoratski, Alexei. "Survival Probability¶in Rank-One Perturbation Problems." Communications in Mathematical Physics 223, no. 1 (2001): 205–22. http://dx.doi.org/10.1007/s002200100541.

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25

Cole, Stephen R., and Miguel A. Hernán. "Adjusted survival curves with inverse probability weights." Computer Methods and Programs in Biomedicine 75, no. 1 (2004): 45–49. http://dx.doi.org/10.1016/j.cmpb.2003.10.004.

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26

Militello, B. "Governing Survival Probability to Distill Quantum States." Optics and Spectroscopy 99, no. 3 (2005): 438. http://dx.doi.org/10.1134/1.2055940.

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27

Ben-Haim, Yakov. "Robust satisficing and the probability of survival." International Journal of Systems Science 45, no. 1 (2012): 3–19. http://dx.doi.org/10.1080/00207721.2012.684906.

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28

Liu, Sheen, Peter Woodlock, Howard Qi, and Yan Alice Xie. "Cash Reserve and Venture Business Survival Probability." Journal of Entrepreneurial Finance 11, no. 3 (2006): 123–36. http://dx.doi.org/10.57229/2373-1761.1042.

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29

Kim, Jeong-Hoon, Yong-Ki Ma, and Chan Yeol Park. "Joint survival probability via truncated invariant copula." Chaos, Solitons & Fractals 85 (April 2016): 68–76. http://dx.doi.org/10.1016/j.chaos.2016.01.012.

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30

Fletcher, R. S. "The gap survival probability and diffractive dissociation." Physics Letters B 320, no. 3-4 (1994): 373–76. http://dx.doi.org/10.1016/0370-2693(94)90672-6.

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31

Mansur-Azzam, Nura, Zeinab Hosseinidoust, Su Gyeong Woo, Renata Vyhnalkova, Adi Eisenberg, and Theo G. M. van de Ven. "Bacteria survival probability in bactericidal filter paper." Colloids and Surfaces B: Biointerfaces 117 (May 2014): 383–88. http://dx.doi.org/10.1016/j.colsurfb.2014.03.011.

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32

Kennedy, Alexander W., Maurie Markman, Charles V. Biscotti, Jonathan D. Emery, and Lisa A. Rybicki. "Survival Probability in Ovarian Clear Cell Adenocarcinoma." Gynecologic Oncology 74, no. 1 (1999): 108–14. http://dx.doi.org/10.1006/gyno.1999.5445.

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33

Parashar, Ranjan, Brinda Patel, Vishal Seán Baveja, Swapnil Paliwal, Rekha Mehani, and Hemlata Parashar. "Morbidity and Survival Probability in Burn Patients." Journal of Indian Academy of Forensic Medicine 46, no. 3 (2024): 379–82. https://doi.org/10.1177/09710973251316353.

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Burn constitutes a major public health problem, especially in low or middle-income countries where over 95% of all burn deaths occur. According to the World Health Organisation, an estimated 1,95,000 deaths every year are caused by burns, the vast majority occur in low and middle-income countries. The present medico-legal study aimed to assess the cause of death and rate of survival related to different types of burn injuries. This autopsy-based descriptive study was carried out at the mortuary of People’s College of Medical Sciences & Research Centre (PCMS & RC), Bhopal, and Medico-le

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34

Ren, Kan, Jiarui Qin, Lei Zheng, et al. "Deep Recurrent Survival Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4798–805. http://dx.doi.org/10.1609/aaai.v33i01.33014798.

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Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential p

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35

Muzaffar, Mahvish. "Conditional survival probability of patients with pancreatic cancer." Journal of Clinical Oncology 30, no. 15_suppl (2012): e14693-e14693. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.e14693.

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e14693 Background: Conditional survival gives more accurate estimate of survival probability for patients who have survived one or more years since initial diagnosis. The aim of this study was to analyze conditional survival probability for pancreatic cancer patients and impact of gender, age and extent of disease on conditional survival. Methods: Using the Surveillance, Epidemiology, and End Results database we analyzed 57,409 patients with pancreatic cancer diagnosed between 1990 and 2008. SEER*Stat: Version7.0.5 software was used to calculate conditional survival, defined as the calculated

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36

Kamal, Oussouadi, and Cherkaoui Kenza. "Survival dynamics of SMES supported by credit guarantee schemes: Insights from Morocco." Banks and Bank Systems 19, no. 1 (2024): 86–98. http://dx.doi.org/10.21511/bbs.19(1).2024.08.

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The aim of this study is to assess the viability of SMEs that had benefited from bank loans backed by credit guarantee schemes. A quantitative approach has been adopted by the study. The sample comprised 398 Moroccan SMEs that had benefited from this type of financing, and the primary objective was to examine their survival over the ten years following the obtaining of these guarantees. Logistic regression was used to reflect several results. The results of the study highlight several factors influencing the probability of survival of these SMEs. Larger amounts of credit promote financial resi

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37

Railsback, Steven F., and Bret C. Harvey. "Representing mortality risk in mechanistic models." Individual-based Ecology 1 (March 27, 2025): e141005. https://doi.org/10.3897/ibe.1.141005.

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Mortality risk is a critical and complex component of individual fitness and individual-based ecology, especially when risk-avoidance behaviors are considered. Organisms are subject to multiple kinds of risk that can vary with habitat, time, individual state, individual activity and behavior, and population status. Yet risk is often represented very simply in models and there is little literature on practical ways to model its variation. In our experience, desirable characteristics of risk models include: (a) survival probability can vary with multiple variables of individuals, habitat, and ot

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38

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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39

Riou, Bruno, Paul Landais, Benoît Vivien, Philippe Stell, Iheb Labbene, and Pierre Carli. "Distribution of the Probability of Survival Is a Strategic Issue for Randomized Trials in Critically Ill Patients." Anesthesiology 95, no. 1 (2001): 56–63. http://dx.doi.org/10.1097/00000542-200107000-00014.

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Background Many randomized clinical trials in trauma have failed to demonstrate a significant improvement in survival rate. Using a trauma patient database, we simulated what could happen in a trial designed to improve survival rate in this setting. Methods The predicted probability of survival was assessed using the TRISS methodology in 350 severely injured trauma patients. Using this probability of survival, the authors simulated the effects of a drug that may increase the probability of survival by 10-50% and calculated the number of patients to be included in a triad, assuming alpha = 0.05

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40

Mi, Jie. "Maximization of a survival probability and its application." Journal of Applied Probability 31, no. 4 (1994): 1026–33. http://dx.doi.org/10.2307/3215326.

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When a mission is assigned, it often is the case that the component used to perform the task is required to work properly during the period of the mission time. In other words, the probability of the event that this component does not fail within the allowable mission time should be as large as possible. This problem is considered for the case when the lifetime of a component has a bathtub-shaped failure rate function, and it is found that burn-in procedure is beneficial. An application of this result to the problem of minimizing the mean number of failures in a given period of mission time is

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41

Kim, Dong Wook, and Byoung Gon Kim. "Korean Listed SMEs Ownership Structure and Survival Probability." Journal of Industrial Economics and Business 32, no. 6 (2019): 2317–35. http://dx.doi.org/10.22558/jieb.2019.12.32.6.2317.

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42

Kang, Yung-Gyung, and Jeong-Man Park. "Survival Probability of Quasi-Species under Environmental Changes." Journal of the Korean Physical Society 53, no. 2 (2008): 868–72. http://dx.doi.org/10.3938/jkps.53.868.

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43

Pozzoli, Gaia, and Benjamin De Bruyne. "Survival probability of random walks leaping over traps." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 12 (2021): 123203. http://dx.doi.org/10.1088/1742-5468/ac3e6f.

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Abstract We consider one-dimensional discrete-time random walks (RWs) in the presence of finite size traps of length ℓ over which the RWs can jump. We study the survival probability of such RWs when the traps are periodically distributed and separated by a distance L. We obtain exact results for the mean first-passage time and the survival probability in the special case of a double-sided exponential jump distribution. While such RWs typically survive longer than if they could not leap over traps, their survival probability still decreases exponentially with the number of steps. The decay rate

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44

Li, Zenghu, and Yaping Zhu. "Survival probability for super-Brownian motion with absorption." Statistics & Probability Letters 186 (July 2022): 109460. http://dx.doi.org/10.1016/j.spl.2022.109460.

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45

Kiesel, Rüdiger, and Luitgard Veraart. "A note on the survival probability in CreditGrades." Journal of Credit Risk 4, no. 2 (2008): 65–74. http://dx.doi.org/10.21314/jcr.2008.070.

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46

Jiang, Jinglu, Congming Mu, Juan Peng, and Jinqiang Yang. "Real options maximizing survival probability under incomplete markets." Quantitative Finance 19, no. 11 (2019): 1921–31. http://dx.doi.org/10.1080/14697688.2019.1617891.

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47

Chen, Robert, Ilie Grigorescu, and Larry Shepp. "Maximizing the discounted survival probability in Vardi's casino." Stochastics 83, no. 4-6 (2011): 623–38. http://dx.doi.org/10.1080/17442508.2011.552980.

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48

MoGonigal, Michael D., John Cole, Donald R. Kauder, et al. "A NEW APPROACH TO PROBABILITY OF SURVIVAL SCORING." Journal of Trauma: Injury, Infection, and Critical Care 33, no. 1 (1992): 156. http://dx.doi.org/10.1097/00005373-199207000-00061.

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49

Mi, Jie. "Maximization of a survival probability and its application." Journal of Applied Probability 31, no. 04 (1994): 1026–33. http://dx.doi.org/10.1017/s002190020009954x.

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When a mission is assigned, it often is the case that the component used to perform the task is required to work properly during the period of the mission time. In other words, the probability of the event that this component does not fail within the allowable mission time should be as large as possible. This problem is considered for the case when the lifetime of a component has a bathtub-shaped failure rate function, and it is found that burn-in procedure is beneficial. An application of this result to the problem of minimizing the mean number of failures in a given period of mission time is

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50

Nan, Wang, Zhao En-Guang, Li Wen-Fei, et al. "Study of Survival Probability of Super Heavy Nuclei." Communications in Theoretical Physics 40, no. 2 (2003): 199–202. http://dx.doi.org/10.1088/0253-6102/40/2/199.

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