Relevant bibliographies by topics / Survival analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Survival analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 November 2024

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Journal articles on the topic "Survival analysis"

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V Kannan R, Pavithra. "Impact of Influential Observations in Survival Analysis." International Journal of Science and Research (IJSR) 12, no. 4 (April 5, 2023): 1057–62. http://dx.doi.org/10.21275/mr23410105218.

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Kim, J. "Survival Analysis." Pediatrics in Review 33, no. 4 (April 1, 2012): 172–74. http://dx.doi.org/10.1542/pir.33-4-172.

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Stolberg, Harald O., Geoffrey Norman, and Isabelle Trop. "Survival Analysis." American Journal of Roentgenology 185, no. 1 (July 2005): 19–22. http://dx.doi.org/10.2214/ajr.185.1.01850019.

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Ziegel, Eric R., M. Parmar, and D. Machin. "Survival Analysis." Technometrics 39, no. 1 (February 1997): 112. http://dx.doi.org/10.2307/1270802.

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Kim, Julia. "Survival Analysis." Pediatrics In Review 33, no. 4 (April 1, 2012): 172–74. http://dx.doi.org/10.1542/pir.33.4.172.

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Ravishanker, Nalini. "Survival Analysis." Technometrics 46, no. 1 (February 2004): 111–12. http://dx.doi.org/10.1198/tech.2004.s740.

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Flynn, Robert. "Survival analysis." Journal of Clinical Nursing 21, no. 19pt20 (August 4, 2012): 2789–97. http://dx.doi.org/10.1111/j.1365-2702.2011.04023.x.

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Miller, Rupert G. "Survival Analysis." IEEE Transactions on Reliability 35, no. 3 (August 1986): 315. http://dx.doi.org/10.1109/tr.1986.4335441.

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Freedman, David A. "Survival Analysis." American Statistician 62, no. 2 (May 2008): 110–19. http://dx.doi.org/10.1198/000313008x298439.

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Streiner, David L., and Geoffrey R. Norman. "Survival analysis." Community Oncology 7, no. 10 (October 2010): 477–79. http://dx.doi.org/10.1016/s1548-5315(11)70429-6.

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Dissertations / Theses on the topic "Survival analysis"

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Wardak, Mohammad Alif. "Survival analysis." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2810.

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Survival analysis pertains to a statistical approach designed to take into account the amount of time an experimental unit contributes to a study. A Mayo Clinic study of 418 Primary Biliary Cirrhosis patients during a ten year period was used. The Kaplan-Meier Estimator, a non-parametric statistic, and the Cox Proportional Hazard methods were the tools applied. Kaplan-Meier results include total values/censored values.
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Abrams, Keith Rowland. "Bayesian survival analysis." Thesis, University of Liverpool, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316744.

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In cancer research the efficacy of a new treatment is often assessed by means of a clinical trial. In such trials the outcome measure of interest is usually time to death from entry into the study. The time to intermediate events may also be of interest, for example time to the spread of the disease to other organs (metastases). Thus, cancer clinical trials can be seen to generate multi-state data, in which patients may be in anyone of a finite number of states at a particular time. The classical analysis of data from cancer clinical trials uses a survival regression model. This type of model allows for the fact that patients in the trial will have been observed for different lengths of time and for some patients the time to the event of interest will not be observed (censored). The regression structure means that a measure of treatment effect can be obtained after allowing for other important factors. Clinical trials are not conducted in isolation, but are part of an on-going learning process. In order to assess the current weight of evidence for the use of a particular treatment a Bayesian approach is necessary. Such an approach allows for the formal inclusion of prior information, either in the form of clinical expertise or the results from previous studies, into the statistical analysis. An initial Bayesian analysis, for a single non-recurrent event, can be performed using non-temporal models that consider the occurrence of events up to a specific time from entry into the study. Although these models are conceptually simple, they do not explicitly allow for censoring or covariates. In order to address both of these deficiencies a Bayesian fully parametric multiplicative intensity regression model is developed. The extra complexity of this model means that approximate integration techniques are required. Asymptotic Laplace approximations and the more computer intensive Gauss-Hermite quadrature are shown to perform well and yield virtually identical results. By adopting counting process notation the multiplicative intensity model is extended to the multi-state scenario quite easily. These models are used in the analysis of a cancer clinical trial to assess the efficacy of neutron therapy compared to standard photon therapy for patients with cancer of the pelvic region. In this trial there is prior information both in the form of clinical prior beliefs and results from previous studies. The usefulness of multi-state models is also demonstrated in the analysis of a pilot quality of life study. Bayesian multi-state models are shown to provide a coherent framework for the analysis of clinical studies, both interventionist and observational, yielding clinically meaningful summaries about the current state of knowledge concerning the disease/treatment process.
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Baade, Ingrid Annette. "Survival analysis diagnostics." Thesis, Queensland University of Technology, 1997.

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林國輝 and Kwok-fai Lam. "Topics in survival analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B30408994.

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Lam, Kwok-fai. "Topics in survival analysis /." Hong Kong : University of Hong Kong, 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13829919.

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Yuan, Lin. "Bayesian nonparametric survival analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22253.pdf.

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North, Bernard. "Contributions to survival analysis." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266152.

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Nhogue, Wabo Blanche Nadege. "Hedge Funds and Survival Analysis." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/26257.

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Using data from Hedge Fund Research, Inc. (HFR), this study adapts and expands on existing methods in survival analysis in an attempt to investigate whether hedge funds mortality can be predicted on the basis of certain hedge funds characteristics. The main idea is to determine the characteristics which contribute the most to the survival and failure probabilities of hedge funds and interpret them. We establish hazard models with time-independent covariates, as well as time-varying covariates to interpret the selected hedge funds characteristics. Our results show that size, age, performance, strategy, annual audit, fund offshore and fund denomination are the characteristics that best explain hedge fund failure. We find that 1% increase in performance decreases the hazard by 3.3%, the small size and the less than 5 years old hedge funds are the most likely to die and the event-driven strategy is the best to use as compare to others. The risk of death is 0.668 times lower for funds who indicated that an annual audit is performed as compared to the funds who did not indicated that an annual audit is performed. The risk of death for the offshore hedge funds is 1.059 times higher than the non-offshore hedge funds.
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Salter, Amy Beatrix. "Multivariate dependencies in survival analysis." Title page, contents and introduction only, 1999. http://web4.library.adelaide.edu.au/theses/09PH/09phs177.pdf.

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Bibliography: leaves 177-181. This thesis investigates determinants of factors associated with retention of injecting drug users on the South Australian methadone program over the decade 1981 to mid 1991. Truncated multivariate survival models are proposed for the analysis of data from the program, and the theory of graphical chain models applied to the data. A detailed analysis is presented which gives further insight into the nature of the relationships that exist amongst these data. This provides an application of graphical chain models to survival data.
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Wienke, Andreas. "Frailty models in survival analysis." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=985529598.

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Books on the topic "Survival analysis"

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O'Quigley, John. Survival Analysis. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-33439-0.

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Liu, Xian. Survival Analysis. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118307656.

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Machin, David, Yin Bun Cheung, and Mahesh KB Parmar. Survival Analysis. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470034572.

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Kleinbaum, David G., and Mitchel Klein. Survival Analysis. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-6646-9.

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Klein, John P., and Melvin L. Moeschberger. Survival Analysis. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/b97377.

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Kleinbaum, David G. Survival Analysis. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2555-1.

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Klein, John P., and Melvin L. Moeschberger. Survival Analysis. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2728-9.

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Kleinbaum, David G., and Mitchel Klein. Survival Analysis. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/0-387-29150-4.

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Guo, Shenyang. Survival analysis. New York: Oxford University Press, 2010.

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Guo, Shenyang. Survival analysis. New York: Oxford University Press, 2010.

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Book chapters on the topic "Survival analysis"

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Tattar, Prabhanjan Narayanachar, and H. J. Vaman. "Survival Trees." In Survival Analysis, 191–216. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003306979-8.

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Fabri, Peter J. "Survival Analysis." In Measurement and Analysis in Transforming Healthcare Delivery, 131–37. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40812-5_9.

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Jiang, Hongyu, and Jason P. Fine. "Survival Analysis." In Topics in Biostatistics, 303–18. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-530-5_15.

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Venables, W. N., and B. D. Ripley. "Survival Analysis." In Modern Applied Statistics with S-Plus, 267–300. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4899-2819-1_11.

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Collett, D. "Survival analysis." In Modelling Survival Data in Medical Research, 1–13. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3115-3_1.

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Holmes, William H., and William C. Rinaman. "Survival Analysis." In Statistical Literacy for Clinical Practitioners, 423–50. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12550-3_16.

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Venables, W. N., and B. D. Ripley. "Survival Analysis." In Statistics and Computing, 343–80. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2719-7_12.

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Friedman, Lawrence M., Curt D. Furberg, and David L. DeMets. "Survival Analysis." In Fundamentals of Clinical Trials, 269–91. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1586-3_15.

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Friedman, Lawrence M., Curt D. Furberg, and David L. DeMets. "Survival Analysis." In Fundamentals of Clinical Trials, 223–45. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2915-3_14.

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Venables, W. N., and B. D. Ripley. "Survival Analysis." In Statistics and Computing, 367–400. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-3121-7_12.

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Conference papers on the topic "Survival analysis"

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Asthana, Siddhartha, Pushpendra Singh, and Parul Gupta. "Survival Analysis." In CHI '15: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2702123.2702428.

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Chapfuwa, Paidamoyo, Chunyuan Li, Nikhil Mehta, Lawrence Carin, and Ricardo Henao. "Survival cluster analysis." In ACM CHIL '20: ACM Conference on Health, Inference, and Learning. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3368555.3384465.

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Wang, Lu, Yan Li, Jiayu Zhou, Dongxiao Zhu, and Jieping Ye. "Multi-task Survival Analysis." In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 2017. http://dx.doi.org/10.1109/icdm.2017.58.

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Vallapuram, Anish K., Nikhil Nanda, Young D. Kwon, and Pan Hui. "Interpretable business survival prediction." In ASONAM '21: International Conference on Advances in Social Networks Analysis and Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3487351.3488353.

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Luo, Jinyuan, Zikai Xiao, Wen Shi, Linhai Xie, Hong Yang, Xiaoxia Yin, and Yanchun Zhang. "CVaDeS: A Conditional Variational Deep Survival Model for Survival Analysis." In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 2023. http://dx.doi.org/10.1109/icdm58522.2023.00055.

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Karabey, Uğur, and Nihal Ata Tutkun. "Model selection criterion in survival analysis." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4992296.

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Chin, Si-Chi, and W. Nick Street. "Survival analysis of click logs." In the 35th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2348283.2348513.

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TANJI, HAJIME, KIRI HIRORIDE, and NAKAYA TETSUO. "SURVIVAL ANALYSIS OF COASTAL FACILITIES." In Proceedings of the 5th International Conference on APAC 2009. World Scientific Publishing Company, 2009. http://dx.doi.org/10.1142/9789814287951_0067.

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Aziz, Nazrina, and Shahirah Abdul Razak. "Survival analysis in insurance attrition." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2018 (MATHTECH2018): Innovative Technologies for Mathematics & Mathematics for Technological Innovation. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136402.

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Woodward, Alexa, Harsh Bandhey, Jason H. Moore, and Ryan J. Urbanowicz. "Survival-LCS: A Rule-Based Machine Learning Approach to Survival Analysis." In GECCO '24: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3638529.3654154.

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Reports on the topic "Survival analysis"

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O'Sullivan, Margaret, and Thomas R. Fleming. Statistics for the Two-Sample Survival Analysis Problem Based on Product Limit Estimators of the Survival Functions. Fort Belvoir, VA: Defense Technical Information Center, November 1986. http://dx.doi.org/10.21236/ada176956.

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Smith, Steven G., John R. Skalski, and J. Warren Schelechte. Statistical Survival Analysis of Fish and Wildlife Tagging Studies; SURPH.1 Manual - Analysis of Release-Recapture Data for Survival Studies, 1994 Technical Manual. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/654053.

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Kueon, Jessica. A Comparison of Schoenfeld and Generalized Residuals in Survival Analysis. Ames (Iowa): Iowa State University, December 2022. http://dx.doi.org/10.31274/cc-20240624-26.

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Wang, Jian, Joy D. Van Nostrand, Zhili He, Liyou Wu, Ye Deng, Xu Zhang, Jizhong Zhou, and Guanghe Li. Microarray-based analysis of survival of soil microbial community during ozonation. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/986918.

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Bonilla-Mejía, Leonardo, Andrés Felipe García-Suaza, and Monica Roa. Country risk ratings and financial crises 1995-2001: a survival analysis. Bogotá, Colombia: Banco de la República, March 2008. http://dx.doi.org/10.32468/be.499.

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Bouwes, Nick, Charlie Petrosky, and Howard Schaller. Comparative Survival [Rate] Study (CSS); Design and Analysis, 2002 Technical Report. Office of Scientific and Technical Information (OSTI), April 2002. http://dx.doi.org/10.2172/818854.

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Zhao, Zhou, Zhaolun Cai, Tianxiang Jiang, Xiaonan Yin, Bo Zhang, and Xiufeng Chen. Treatment-related adverse events of EZH2 inhibitor therapies in clinical trials: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2023. http://dx.doi.org/10.37766/inplasy2023.3.0028.

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Review question / Objective: In this study, we used conventional Meta-analysis and Network meta-analysis (NMA) to evaluate the survival outcomes of best supportive care only and cytoreductive treatment before transplantation, survival outcomes of different disease states at transplantation, and the effects of each treatment on survival outcomes of MDS, respectively, with a view to comprehensive clinical treatment and individualized treatment of MDS patients before transplantation, providing evidence-based medical evidence, prolonging survival time and improving life quality. Information sources: We will search articles in three electronic database including PubMed, EMBASE and Cochrane Library. All the English publications until 2022 will be searched without any restriction of countries or article type. Reference list of all selected articles will independently screened to identify additional studies left out in the initial search.
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Yang, Jianguo, Fuyu Zhao, Xinpeng Zhou, Yuying sun, Xueping Lun, Jiaojiao Cao, and Bing Fan. Survival and prognosis analysis of systemic lupus erythematosus-pulmonary hypertension: a protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2023. http://dx.doi.org/10.37766/inplasy2023.4.0017.

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Review question / Objective: The study aimed to evaluate survival rates and prognosis in systemic lupus erythematosus (SLE) patients with pulmonary hypertension (PH) using meta-analysis. (P: patients with SLE-PH; I: No intervention; C: No comparator; O: survival and prognosis; S: meta-analysis). Condition being studied: Pulmonary hypertension (PH) is a life-threatening condition characterized by elevated pulmonary arteries pressure due to increased pulmonary vascular resistance1. Symptoms of PH are nonspecific but typically include exertional dyspnea and fatigue. Systemic lupus erythematosus (SLE) is characterized by aberrant immune activity leading to variable clinical manifestations ranging from mild fatigue and joint pain to severe and life-threatening organ damage. Recent data from lupus registries have provided more accurate estimates of SLE incidence and prevalence, which showed Lupus is more common in non-white populations.
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Meng, Wenwen, Haiyan Shi, Jie Liu, Xiaohui Ge, Yafeng Xu, Shiqi Shan, Lin Wang, Juwei Liu, Lin Zha, and Jun Niu. NF-κB expression and survival outcomes in gastric cancer: A meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2020. http://dx.doi.org/10.37766/inplasy2020.8.0037.

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McKeague, Ian W., and Mei-Jie Zhang. On the Analysis of Grouped Survival Data Using Cumulative Occurrence/Exposure Rates. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada238219.

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