Relevant bibliographies by topics / Survival Data

Contents

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

Academic literature on the topic 'Survival Data'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 November 2024

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Journal articles on the topic "Survival Data"

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Survival Data"

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Fontenelle, OtÃvio Fernandes. "Survival Analysis; Micro and Small Enterprises; Modeling Survival Data, Data Characterization Survival; parametric Estimator KAPLAN-MEIER." Universidade Federal do CearÃ, 2009. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=4173.

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nÃo hÃ
The main objective of this research is to explore economics issues that may induce impact on lifetime of small businesses during 2002 to 2006. The group of enterprises studied was selected from database of taxpayers recorded at fiscal authority of State of CearÃ. To do that, the methodology was focused on a branch of statistics which deals with survival analysis, called duration analysis or duration modeling in economics. It was applied non-linear model whose non-parametric estimator chosen was KAPLAN-MEIER. Through that methodology, it was developed sceneries based on the following attributes: county where the enterprises were established; economics activities based on national classification, fiscal version 1.0/1.1; and, finally, the relationship between State of Cearà â as fiscal authority â and enterprises. The counties were grouped applying two parameters of stratifications: gross domestic product(GDP) per capita and investment in education per capita. Before any stratification, only counties with thirty or more enterprises starting their activities in year 2002 were considered in sceneries to analysis.
A dissertaÃÃo tem o objetivo de investigar fatores econÃmicos que possam influenciar na sobrevida de micros e pequenas empresas (MEPs) contribuintes do Imposto sobre OperaÃÃes relativas à CirculaÃÃo de Mercadorias e sobre PrestaÃÃes de ServiÃos de Transporte Interestadual e Intermunicipal e de ComunicaÃÃo (ICMS) do Estado do Cearà no perÃodo de 2002 à 2006. Para isso, aplicou-se uma tÃcnica estatÃstica denominada anÃlise de sobrevivÃncia a partir de modelos nÃo lineares cujo estimador nÃo-paramÃtrico escolhido foi o de KAPLAN-MEIER. Com os dados de sobrevivÃncia devidamente modelados, buscou-se estratificÃ-los focando os municÃpios dos logradouros das MEPs; dentro do que tange as operaÃÃes do ICMS, focando as atividades econÃmicas segundo a classificaÃÃo nacional de atividades econÃmicas (CNAE) versÃo fiscal 1.0/1.1; e, finalmente, observar a relaÃÃo do Estado â enquanto autoridade fiscal â com esses pequenos estabelecimentos, restringindo temporariamente seu faturamento ou mesmo baixando sua inscriÃÃo estadual, impossibilitando a continuidade de suas atividades. Dos municÃpios, utilizou-se como Ãndice de estratificaÃÃo entre as curvas de sobrevivÃncia o produto interno bruto (PIB) per capita e os investimentos mÃdio per capita em educaÃÃo daquelas empresas localizadas em municÃpios com 30 ou mais estabelecimentos ativados no ano de 2002. Dentre outras, duas importantes observaÃÃes foram identificar o municÃpio de Fortaleza como um âoutlinerâ frente aos outros municÃpios e a forte dominÃncia da curva de sobrevivÃncia das empresas que nÃo sofreram intervenÃÃo do fisco em suas atividades sobre aquelas que tiveram.
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Pause, Cheryl A. "Frailty misspecification in survival data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0027/NQ31107.pdf.

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Bruno, Rexanne Marie. "Statistical Analysis of Survival Data." UNF Digital Commons, 1994. http://digitalcommons.unf.edu/etd/150.

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The terminology and ideas involved in the statistical analysis of survival data are explained including the survival function, the probability density function, the hazard function, censored observations, parametric and nonparametric estimations of these functions, the product limit estimation of the survival function, and the proportional hazards estimation of the hazard function with explanatory variables. In Appendix A these ideas are applied to the actual analysis of the survival data for 54 cervical cancer patients.
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Ma, Yehan. "Simultaneous Inference on Survival Data." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1555426605969823.

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Christiansson, Alexander. "Classication of survival data by comparison of survival functions : an application to prostate cancer registry data." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420703.

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Zhang, Xun 1959. "Estimating survival from partially observed data." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31563.

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Often, in cross-sectional-follow-up studies, survival data are obtained from prevalent cases only. This sampling mechanism introduces length-bias. An added difficulty is that in some cases the times of onset cannot be ascertained or are recorded with great uncertainty. Such was the situation in the Canadian Study of Health and Aging, a nation wide study of dementia conducted by Health Canada during 1991 and 1996. This thesis proposes methods to estimate the survival function nonparametrically, when the data are length-biased and only partially observed. By using the "forward recurrence times" only, we show how one can overcome the difficulty caused by missing onset times, while by using the "backward recurrence times" only, one can avoid the cost and effort of follow-up. We illustrate our methods through an application to data derived from the CSHA.
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Guo, Hui 1974. "Doubly censored prevalent cohort survival data." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101131.

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When survival data are collected as part of a prevalent cohort study, the recruited cases have already experienced their initiating event. When these initiating times are generated from a Stationary Poisson Process, these survival data are termed "length-biased". Our interest is to estimate the Survival function, from the initiating event time, which is often known only with uncertainty, to the failure or censoring time. We derive the likelihood function and propose two methods to estimate the survival function incorporating the possible uncertainty. We verify the methods by the means of simulation. We also illustrate the methods using a real set of data on survival with dementia collected as part of the Canadian Study of Health and Aging (CSHA).
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Shimakura, Silvia Emiko. "Statistical methods for spatial survival data." Thesis, Lancaster University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418824.

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葉英傑 and Ying-Kit David Ip. "Analysis of clustered grouped survival data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226127.

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梁翠蓮 and Tsui-lin Leung. "Proportional odds model for survival data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B42575011.

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Books on the topic "Survival Data"

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Cox, D. R. Analysis of survival data. London: Chapman and Hall, 1990.

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Hougaard, Philip. Analysis of Multivariate Survival Data. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1304-8.

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Elandt-Johnson, Regina C., and Norman L. Johnson. Survival Models and Data Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1999. http://dx.doi.org/10.1002/9781119011040.

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Elandt-Johnson, Regina C. Survival models and data analysis. New York: Wiley, 1999.

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Huang, Tzu Jung. Marginal Screening on Survival Data. [New York, N.Y.?]: [publisher not identified], 2017.

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Lee, Elisa T., and John Wenyu Wang. Statistical Methods for Survival Data Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2003. http://dx.doi.org/10.1002/0471458546.

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Hanagal, David D. Modeling Survival Data Using Frailty Models. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1181-3.

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

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Wenyu, Wang John, ed. Statistical methods for survival data analysis. 3rd ed. New York: J. Wiley, 2003.

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Hanagal, David D. Modeling survival data using frailty models. Boca Raton, FL: Chapman & Hall/CRC, 2011.

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Book chapters on the topic "Survival Data"

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Cox, D. R. "Survival Data." In International Encyclopedia of Statistical Science, 1580–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_585.

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Nokeri, Tshepo Chris. "Survival Analysis." In Data Science Revealed, 185–200. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6870-4_11.

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DeMaris, Alfred, and Steven H. Selman. "Survival Analysis." In Converting Data into Evidence, 137–59. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7792-1_8.

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Tattar, Prabhanjan Narayanachar, and H. J. Vaman. "Lifetime Data and Concepts." In Survival Analysis, 3–26. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003306979-1.

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Collett, D. "Modelling survival data." In Modelling Survival Data in Medical Research, 53–106. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3115-3_3.

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Melnick, Edward L. "Modeling Survival Data." In International Encyclopedia of Statistical Science, 841–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_370.

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Hougaard, Philip. "Univariate Survival Data." In Statistics for Biology and Health, 36–111. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1304-8_2.

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Ibrahim, Joseph G., Ming-Hui Chen, and Debajyoti Sinha. "Missing Covariate Data." In Bayesian Survival Analysis, 290–319. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3447-8_8.

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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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Collett, David. "Survival analysis." In Modelling Survival Data in Medical Research, 1–14. 4th ed. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003282525-1.

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Conference papers on the topic "Survival Data"

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Özbay, Fatma. "A Dimensional Approach for Multivariate Discrete Time Survival Data." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10711056.

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Li, Mengke, Chenyang Wu, Xiaole Zuo, Lanlan Cheng, and Jingzhuo Zhang. "Multi-Modal Data Fusion Algorithm for Cancer Survival Analysis." In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/icecet61485.2024.10698739.

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Das, Sukanya, Rohan Biswas, Subhamita Mukherjee, and Subhajit Bhowmick. "Water Quality Detection for the Survival of Aquatic Beings." In 2024 International Conference on Data Science and Network Security (ICDSNS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691242.

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Fedeli, Stefano, Frida Schain, Sana Imtiaz, Zainab Abbas, and Vladimir Vlassov. "Privacy Preserving Survival Prediction." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9672036.

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Bagheri, Arezoo. "Analyzing determinants of marriage survival by random survival forests." In INTERNATIONAL CONFERENCE ON STATISTICS AND DATA SCIENCE 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0112585.

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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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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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Jing, How, and Alexander J. Smola. "Neural Survival Recommender." In WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3018661.3018719.

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Lord, Laurel, John Sell, Feyzi Bagirov, and Mark Newman. "Survival Analysis within Stack Overflow: Python and R." In 2018 4th International Conference on Big Data Innovations and Applications (Innovate-Data). IEEE, 2018. http://dx.doi.org/10.1109/innovate-data.2018.00015.

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Xuan Tran, Ha, Thuc Duy Le, Jiuyong Li, Lin Liu, Jixue Liu, Yanchang Zhao, and Tony Waters. "Decision Support for Disability Employment using Counterfactual Survival Analysis." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10021126.

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Reports on the topic "Survival Data"

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Wang, Jane-Ling, and Thomas P. Hettmansperger. Two-Sample Inference for Median Survival Times Based on One-Sample Procedures for Censored Survival Data. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada197776.

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Hudomiet, Péter, and Robert Willis. Estimating Second Order Probability Beliefs from Subjective Survival Data. Cambridge, MA: National Bureau of Economic Research, July 2012. http://dx.doi.org/10.3386/w18258.

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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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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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Kimura, Fukunari, and Takamune Fujii. Globalizing Activities and the Rate of Survival: Panel Data Analysis on Japanese Firms. Cambridge, MA: National Bureau of Economic Research, November 2003. http://dx.doi.org/10.3386/w10067.

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Volpe Martincus, Christian, and Jerónimo Carballo. Survival of New Exporters in Developing Countries: Does it Matter How They Diversify? Inter-American Development Bank, December 2008. http://dx.doi.org/10.18235/0011058.

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Recent studies have shown that developing countries might have significantly better export performance if they were able to increase the duration of their trade relationships. Evidence on duration of these relationships at the firm level is virtually absent. In this paper, we aim at filling this gap in the literature by analyzing what determines export survival using firm-level data for the whole population of Peruvian new exporters over the period 2000-2006. In particular, we address one question: Does it matter how firms diversify? We find that geographical diversification increases the probability of survival in export markets more than product diversification.
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Lichtenberg, Frank. Are Increasing 5-Year Survival Rates Evidence of Success Against Cancer? A reexamination using data from the U.S. and Australia. Cambridge, MA: National Bureau of Economic Research, June 2010. http://dx.doi.org/10.3386/w16051.

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Cutherell, David, and Wendy Cass. Survival of replanted American ginseng (Panax quinquefolius) in Shenandoah National Park. National Park Service, 2024. http://dx.doi.org/10.36967/2301890.

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American ginseng (Panax quinquefolius) is a perennial herb of eastern deciduous forests that is harvested for medicinal use. Because of the high market value of its roots, ginseng is threatened in much of its range and is a target for poaching in national parks. In Shenandoah National Park, illegally harvested roots confiscated by law enforcement are replanted in the Park to salvage the roots and reestablish ginseng populations. Ninety-five sites where roots were replanted between 1998 and 2017 were surveyed to determine the plants? survival rate. Population growth was measured as the total number of plants found at each site divided by the number of roots originally replanted. Average population growth across sites was found to be 0.60, or 60% of the number of replanted roots. Ninety-one percent of sites contained at least one plant, and at least 42% contained juvenile plants, indicating that these populations were reproducing. Habitat data collected at each site were analyzed to determine what type of plant communities contained the most successful populations, but the results were inconclusive. Since root condition prior to replanting is likely to be the most important factor in determining survival of replanted roots, basic guidelines were summarized for staff involved in handling ginseng. Twenty-eight 4 x 10 m covert ginseng plots were also resampled 14 years after installation. Plant counts were similar between samples likely indicating consistent poaching pressure.
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Shumway, Dean A., Kimberly S. Corbin, Magdoleen H. Farah, Kelly E. Viola, Tarek Nayfeh, Samer Saadi, Vishal Shah, et al. Partial Breast Irradiation for Breast Cancer. Agency for Healthcare Research and Quality (AHRQ), January 2023. http://dx.doi.org/10.23970/ahrqepccer259.

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Objectives. To evaluate the comparative effectiveness and harms of partial breast irradiation (PBI) compared with whole breast irradiation (WBI) for early-stage breast cancer, and how differences in effectiveness and harms may be influenced by patient, tumor, and treatment factors, including treatment modality, target volume, dose, and fractionation. We also evaluated the relative financial toxicity of PBI versus WBI. Data sources. MEDLINE®, Embase®, Cochrane Central Registrar of Controlled Trials, Cochrane Database of Systematic Reviews, Scopus, and various grey literature sources from database inception to June 30, 2022. Review methods. We included randomized clinical trials (RCTs) and observational studies that enrolled adult women with early-stage breast cancer who received one of six PBI modalities: multi-catheter interstitial brachytherapy, single-entry catheter brachytherapy (also known as intracavitary brachytherapy), 3-dimensional conformal external beam radiation therapy (3DCRT), intensity-modulated radiation therapy (IMRT), proton radiation therapy, intraoperative radiotherapy (IORT). Pairs of independent reviewers screened and appraised studies. Results. Twenty-three original studies with 17,510 patients evaluated the comparative effectiveness of PBI, including 14 RCTs, 6 comparative observational studies, and 3 single-arm observational studies. PBI was not significantly different from WBI in terms of ipsilateral breast recurrence (IBR), overall survival, or cancer-free survival at 5 and 10 years (high strength of evidence [SOE]). Evidence for cosmetic outcomes was insufficient. Results were generally consistent when PBI modalities were compared with WBI, whether compared individually or combined. These PBI approaches included 3DCRT, IMRT, and multi-catheter interstitial brachytherapy. Compared with WBI, 3DCRT showed no difference in IBR, overall survival, or cancer-free survival at 5 and 10 years (moderate to high SOE); IMRT showed no difference in IBR or overall survival at 5 and 10 years (low SOE); multi-catheter interstitial brachytherapy showed no difference in IBR, overall survival, or cancer-free survival at 5 years (low SOE). Compared with WBI, IORT was associated with a higher IBR rate at 5, 10, and over 10 years (high SOE), with no difference in overall survival, cancer-free survival, or mastectomy-free survival (low to high SOE). There were significantly fewer acute adverse events (AEs) with PBI compared with WBI, with no apparent difference in late AEs (moderate SOE). Data about quality of life were limited. Head-to-head comparisons between the different PBI modalities showed insufficient evidence to estimate an effect on main outcomes. There were no significant differences in IBR or other outcomes according to patient, tumor, and treatment characteristics; however, data for subgroups were insufficient to draw conclusions. Eight studies addressed concepts closely related to financial toxicity. Compared with conventionally fractionated WBI, accelerated PBI was associated with lower transportation costs and days away from work. PBI was also associated with less subjective financial difficulty at various time points after radiotherapy. Conclusions. Clinical trials that compared PBI with WBI demonstrate no significant difference in the risk of IBR. PBI is associated with fewer acute AEs and may be associated with less financial toxicity. The current evidence supports the use of PBI in appropriately selected patients with early-stage breast cancer. Further investigation is needed to evaluate the outcomes of PBI in patients with various clinical and tumor characteristics, and to define optimal radiation treatment dose and technique for PBI.
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10

Navarro, Lucas. Entrepreneurship Policy and Firm Performance: Chile's CORFO Seed Capital Program. Inter-American Development Bank, August 2014. http://dx.doi.org/10.18235/0008445.

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This paper uses administrative data from projects for which an application was made to CORFO's (Chile's main development agency) Seed Capital Program (SCP) during the 2008-12 period. This paper analyzes the impact of the program, which is a subsidy provided to new, innovative firms, on Start, Growth, and Survival. The projects are supported by sponsors in the application process. Results of our analysis indicate that, controlling for observed characteristics, projects that received the subsidy had a 9.5 percentage point higher probability of starting to sell. Among created firms, subsidized (Treated) firms showed a 17 percentage point higher probability of significant Growth (in sales) and a similar effect on Survival. The analysis also examined a change in the program rules in 2011, whereby sponsors were paid based on performance instead of fixed fees, as it was before 2011. For projects affected by the policy change, results indicate no overall association of the program with the probability of Start and a positive correlation with Growth and Survival. These results could be a consequence of sponsors making better project selections for funding. The change in rules means that the program contributes more to the Growth and Survival of new firms than to their Start, which would take place anyway. The data available for this study did not make it possible to control for potential selection biases, and thus the results need to be viewed with caution, and data collection standards need to improve to help identify the true effect of this type of program.
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