Relevant bibliographies by topics / Survival curves

Contents

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

Academic literature on the topic 'Survival curves'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Survival curves"

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Hess, Aaron S., and John R. Hess. "Kaplan–Meier survival curves." Transfusion 60, no. 4 (February 20, 2020): 670–72. http://dx.doi.org/10.1111/trf.15725.

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Peleg, Micha, and Martin B. Cole. "Reinterpretation of Microbial Survival Curves." Critical Reviews in Food Science and Nutrition 38, no. 5 (July 1998): 353–80. http://dx.doi.org/10.1080/10408699891274246.

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Smith, David W. E. "The tails of survival curves." BioEssays 16, no. 12 (December 1994): 907–11. http://dx.doi.org/10.1002/bies.950161209.

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Chappell, Rick, and Xiaotian Zhu. "Describing Differences in Survival Curves." JAMA Oncology 2, no. 7 (July 1, 2016): 906. http://dx.doi.org/10.1001/jamaoncol.2016.0001.

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Rakow, Tim, Rebecca J. Wright, Catherine Bull, and David J. Spiegelhalter. "Simple and Multistate Survival Curves." Medical Decision Making 32, no. 6 (June 29, 2012): 792–804. http://dx.doi.org/10.1177/0272989x12451057.

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Bender, R., A. Schultz, R. Pichlmayr, and U. Grouven. "Application of Adjusted Survival Curves to Renal Transplant Data." Methods of Information in Medicine 31, no. 03 (1992): 210–14. http://dx.doi.org/10.1055/s-0038-1634871.

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Abstract:An important means in the analysis of survival time data is the estimation and graphical representation of survival probabilities. In this paper unifactorial parametric and non-parametric survival curve estimators and two types of adjusted survival curves based on a parametric multifactorial approach are applied to renal transplant data. It is shown that the resulting survival curves can differ substantially. The unifactorial survival curves yield biased results in case of serious disequilibrium in the data. This drawback of the unifactorial methods has been overcome by the use of adjusted survival curves which take possible distortions in the data set into account. The benefits of adjusted survival curves in assessing potentially prognostic factors are elucidated by the application to data from renal transplantation.
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Zelterman, Daniel, and James W. Curtsinger. "Survival Curves Subjected to Occasional Insults." Biometrics 51, no. 3 (September 1995): 1140. http://dx.doi.org/10.2307/2533013.

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COMFORT, A. "SURVIVAL CURVES OF MAMMALS IN CAPTIVITY." Proceedings of the Zoological Society of London 128, no. 3 (August 20, 2009): 349–64. http://dx.doi.org/10.1111/j.1096-3642.1957.tb00329.x.

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Davies, Charlotte, Andrew Briggs, Paula Lorgelly, Göran Garellick, and Henrik Malchau. "The “Hazards” of Extrapolating Survival Curves." Medical Decision Making 33, no. 3 (March 3, 2013): 369–80. http://dx.doi.org/10.1177/0272989x12475091.

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Albers, W. "Comparing Survival Curves Using Rank Tests." Biometrical Journal 33, no. 2 (1991): 163–72. http://dx.doi.org/10.1002/bimj.4710330205.

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Dissertations / Theses on the topic "Survival curves"

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Bradley, Jeremy. "A reversible jump Markov chain Monte Carlo approach to hazard estimation for competing risks data." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289357.

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Earle, Craig. "Non-small cell lung cancer, assessment of methodologies to combine survival curves in meta-analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ36688.pdf.

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Lourens, Spencer. "Bias in mixtures of normal distributions and joint modeling of longitudinal and time-to-event data with monotonic change curves." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1685.

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Estimating parameters in a mixture of normal distributions dates back to the 19th century when Pearson originally considered data of crabs from the Bay of Naples. Since then, many real world applications of mixtures have led to various proposed methods for studying similar problems. Among them, maximum likelihood estimation (MLE) and the continuous empirical characteristic function (CECF) methods have drawn the most attention. However, the performance of these competing estimation methods has not been thoroughly studied in the literature and conclusions have not been consistent in published research. In this article, we review this classical problem with a focus on estimation bias. An extensive simulation study is conducted to compare the estimation bias between the MLE and CECF methods over a wide range of disparity values. We use the overlapping coefficient (OVL) to measure the amount of disparity, and provide a practical guideline for estimation quality in mixtures of normal distributions. Application to an ongoing multi-site Huntington disease study is illustrated for ascertaining cognitive biomarkers of disease progression. We also study joint modeling of longitudinal and time-to-event data and discuss pattern-mixture and selection models, but focus on shared parameter models, which utilize unobserved random effects in order to "join" a marginal longitudinal data model and marginal survival model in order to assess an internal time-dependent covariate's effect on time-to-event. The marginal models used in the analysis are the Cox Proportional Hazards model and the Linear Mixed model, and both of these models are covered in some detail before defining joints models and describing the estimation process. Joint modeling provides a modeling framework which accounts for correlation between the longitudinal data and the time-to-event data, while also accounting for measurement error in the longitudinal process, which previous methods failed to do. Since it has been shown that bias is incurred, and this bias is proportional to the amount of measurement error, utilizing a joint modeling approach is preferred. Our setting is also complicated by monotone degeneration of the internal covariate considered, and so a joint model which utilizes monotone B-Splines to recover the longitudinal trajectory and a Cox Proportional Hazards (CPH) model for the time-to-event data is proposed. The monotonicity constraints are satisfied via the Projected Newton Raphson Algorithm as described by Cheng et al., 2012, with the baseline hazard profiled out of the $Q$ function in each M-step of the Expectation Maximization (EM) algorithm used for optimizing the observed likelihood. This method is applied to assess Total Motor Score's (TMS) ability to predict Huntington Disease motor diagnosis in the Biological Predictors of Huntington's Disease study (PREDICT-HD) data.
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Zhang, Xin. "Development and validation of a nanodosimetry-based cell survival model for mixed high- and low-LET radiations." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-05222006-140610/.

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Thesis (Ph. D.)--Mechanical Engineering, Georgia Institute of Technology, 2007.
Rahnema, Farzad, Committee Member ; Hertel, Nolan, Committee Member ; Wang, Chris, Committee Chair ; Radhakrishna, Harish, Committee Member ; Al-Sheikhly, Mohamad, Committee Member.
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Linton, Richard Howard. "Use of the Gompertz equation to model non-linear survival curves and predict temperature, pH, and sodium chloride effects for Listeria monocytogenes Scott A." Diss., This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06062008-164544/.

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Montoya, Jose Fernando Guarín. "Eficiência do uso de medidor de atividade eletrônico na detecção de cio de vacas leiteiras de alta produção." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/11/11139/tde-10032008-074613/.

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Grandes avanços tem sido feitos para melhorar a produção de leite, mas estes avanços têm resultado numa queda geral da eficiência reprodutiva. Para se combater esta queda, tem sido seguidos principalmente três caminhos: um relacionado com a vaca, outro com as gametas e outro com o ambiente. Os problemas associados à vaca compreendem os problemas relacionados com a detecção de cio e os problemas hormonais que resultam em baixas taxas de concepção e aumento das mortes embrionárias precoces. Alternativas para melhorar a detecção de cio tem sido procuradas, dentre estas, os medidores de atividade são uma alternativa promissória, porém ainda em desenvolvimento. Com o fim de avaliar a eficiência do uso de medidor de atividade na detecção de cio de vacas leiteiras de alta produção, foram desenvolvidos três experimentos. O primeiro avaliou a eficiência do uso de medidor de atividade na detecção de cio após o período de espera voluntario (PEV), o segundo avaliou a eficiência do uso de medidor de atividade na detecção do cio de retorno após o uso de um protocolo de inseminação artificial a tempo fixo (IATF), o terceiro avaliou a relação custo-benefício da aquisição do aparelho. Na detecção do primeiro cio efetivo após o PEV o medidor de atividade apresentou maior taxa de detecção de cio, comparado com a observação visual de cio. O aparelho mostrou especial avantaja sobre a observação visual de cio na detecção de vacas multíparas de alta produção. No experimento de detecção de cio de retorno após o uso de IATF, o medidor de atividade detectou maior número de animais em menor tempo, porém, as mudanças no padrão de comportamento animal causadas pelos hormônios próprios da sincronização de cio, diminuíram a confiabilidade do sistema automatizado de detecção, ainda assim o numero de animais detectados em cio verdadeiro do sistema automatizado foi superior. Na análise de custo-benefício, a taxa interna de retorno obtida, demonstrou que o medidor de atividade é um investimento rentável o que possibilita sua aquisição. Enquanto aos falsos positivos gerados pelo sistema, sugere-se integrar as indicações feitas pelo sistema automatizado com a experiência dos funcionários das fazendas.
Huge progress has been made to improve the milk yield, but these advances have resulted in a general decrease of reproductive efficiency. Three main ways have been taken to combat this decrease: one related to the cow, another with the gametes and the lasts related with the environment. The problems associated with cow comprehend the estrous detection problems and the hormonal disorders that results in low conception rates and increases the early embryonic deaths. Alternatives to improve the heat detection has been developed. Among them, the activity meters are a promissory alternative, but still in development. Three experiments were developed in order to assess the efficiency of use of an activity meter in the estrous detection of high producing dairy cows. The first assessed the efficiency of the use of activity meter detecting estrous after the voluntary waiting period (VWP), the second assessed the efficiency of use of activity meter in the detection of the return estrous after use of a fixed-time artificial insemination (TAI) protocol, the third assessed the cost-benefit of acquisition of the system. In the first estrous after the VWP, the activity meter presented higher estrous detection rate, compared with the visual observation. The system has special vantage in the detection of multiparous high producing cows, if compared with visual observation. In the experiment of detecting return estrous after use of TAI, the activity meter detected greater number of animals in less time, however, changes in the pattern of animal behavior caused by hormones during the synchronization, decreased the accuracy of the automated system. On the other hand, the number of animals detected in real estrous of the automated system was superior than in visual observation. In the cost-benefit analysis, the internal rate of return obtained, showed that the activity meter is a profitable investment. Referent to the false positives generated by the system, it is suggested to integrate the indications made by the automated system with the experience of the functionaries of the dairy farms.
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Giolo, Suely Ruiz. "Variáveis latentes em análise de sobrevivência e curvas de crescimento." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-09052003-143659/.

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Em um contexto de analise de dados de sobrevivência univariados ou multivariados, dados de tempos de falha caracterizam-se pela possibilidade de poderem ser censurados. Embora comum na pratica, a censura impede o uso de alguns procedimentos estatisticos covencionais o que vem motivando, em especial apos a publicacao do artigo de Cox (1972), o desenvolvimento de metodos estatisticos nessa area. Uma linha de estudo recente e a de que, em algumas situacoes, a variavel resposta esteja sendo inuenciada por variaveis latentes, variaveis estas que sao usadas, em um sentido estatistico, para descreverem efeitos geneticos ou ambientais compartilhados pelos indivduos ou, ainda, covariaveis nao consideradas no estudo. Nesse trabalho, enfase e dada aos modelos de sobrevivencia que consideram tempos de falha multivariados e variaveis latentes. Esses tempos aparecem quando, por exemplo, cada individuo em estudo esta sujeito a diversos eventos ou, quando existe um agrupamento natural ou artificial o qual induz dependencia entre os tempos dos individuos do mesmo grupo. Modelos com variaveis latentes em que tais tempos de falha ocorrem em intervalos de tempo, ou seja, em um contexto de censura intervalar sao especialmente considerados nesse trabalho. O modelo de fragilidade gama para dados de sobrevivencia com censura intervalar e proposto, nesse trabalho, como um criterio para a selecao de bovinos. Como uma alternativa para esta selecao, o modelo de curvas de crescimento com efeitos aleatorios e tambem considerado. Para a estimacao dos parametros envolvidos em ambos os modelos propostos, programas computacionais sao apresentados. Uma abordagem Bayesiana e considerada no processo de estimação sendo, o metodo de Markov chain Monte Carlo (MCMC) utilizado e as distribuicoes a posteriori obtidas, usando-se o amostrador de Gibbs. O modelo de fragilidade gama com censura intervalar e o de curvas de crescimento com efeitos aleatorios sao comparados por meio de um estudo de simulação. Para ilustrar ambos os modelos propostos, estudos com bovinos das racas Nelore e Canchim são utilizados.
In a context of univariate or multivariate survival data analysis, failure times data are characterized by the possibility to be censored. Although common in practice, censoring precludes the use of some conventional statistical procedures and it has been motivating, specially after the publication of the Cox's paper (1972), the development of statistical methods in this area. A recent topic of study is concerned with some situations where the response variable is in uenced by latent variables which are used in a statistical sense to describe genetic or environmental efects shared by individuals or also covariates not considered in the study. In this work emphasis is given to survival models which consider multivariate failure times and latent variables. Such times occur when, for instance, each individual under study is exposed to several events or when there is a natural or artificial clustering that causes dependence among times of those individuals at the same cluster. Models with latent variables where such failure times lie in intervals of time, i.e. in an interval censored context are specially considered in this work. The gamma frailty interval censored survival model is proposed in this work as a selection criterion for cattle. As an alternative selection criterion the growth curves model with random efects is also considered. To estimate the involved parameters in both proposed models, computational programs are presented. A Bayesian approach is considered in the estimation process so that the Markov chain Monte Carlo (MCMC) method is used and the posterior distributions are obtained using Gibbs sampling. The gamma frailty interval-censored survival model and the growth curves model with random efects are compared using a simulation study. To illustrate both proposed models studies with Nelore and Canchim cattle are used.
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Raminelli, Jaqueline Aparecida. "Métodos de adequação e diagnóstico em modelos de sobrevivência dinâmicos." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-03052016-164950/.

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A análise de dados de sobrevivência tem sido tradicionalmente baseada no modelo de regressão de Cox (COX, 1972). No entanto, a suposição de taxas de falha proporcionais assumida para esse modelo pode não ser atendida em diversas situações práticas. Essa restrição do modelo de Cox tem gerado interesse em abordagens alternativas, dentre elas os modelos dinâmicos que permitem efeito das covariáveis variando no tempo. Neste trabalho, foram revisados os principais modelos de sobrevivência dinâmicos com estrutura aditiva e multiplicativa nos contextos não paramétrico e semiparamétrico. Métodos gráficos baseados em resíduos foram apresentados com a finalidade de avaliar a qualidade de ajuste desses modelos. Uma versão tempo-dependente da área sob a curva ROC, denotada por AUC(t), foi proposta com a finalidade de avaliar e comparar a qualidade de predição entre modelos de sobrevivência com estruturas aditiva e multiplicativa. O desempenho da AUC(t) foi avaliado por meio de um estudo de simulação. Dados de três estudos descritos na literatura foram também analisados para ilustrar ou complementar os cenários que foram considerados no estudo de simulação. De modo geral, os resultados obtidos indicaram que os métodos gráficos apresentados para avaliar a adequação dos modelos em conjunto com a AUC(t) se constituem em um conjunto de ferramentas estatísticas úteis para o próposito de avaliar modelos de sobrevivência dinâmicos nos contextos não paramétrico e semiparamétrico. Além disso, a aplicação desse conjunto de ferramentas em alguns conjuntos de dados evidenciou que se, por um lado, os modelos dinâmicos são atrativos por permitirem covariáveis tempo-dependentes, por outro lado podem não ser apropriados para todos os conjuntos de dados, tendo em vista que estimação pode apresentar restrições para alguns deles.
Analysis of survival data has been traditionally based on the Cox regression model (COX, 1972). However, the proportionality of the hazards required by this model may not be attended for many practical situations. This restriction of the Cox model has generated interest in alternative approaches, among them dynamic models that allow covariates with time-varying effect. In this work, the main dynamic survival models with additive and multiplicative structures were revised under the nonparametric and semiparametric settings. Graphical methods based on residuals were presented in order to evaluate the goodness-of-fit of these models. A time-dependent version of the area under the ROC curve, denoted by AUC(t), was proposed to evaluate and compare the predictive accuracy of additive and multiplicative survival models. The performance of the AUC(t) was evaluated by means of a simulation study. Data from three studies described in the literature were also analyzed to illustrate or complement the scenarios that were considered in the simulation study. Overall, the results indicate that the graphical methods presented to assess the goodness-of-fit of the models together with the AUC(t) provide a useful set of statistics tools for the purpose of evaluating dynamic survival models in the nonparametric and semiparametric settings. Moreover, applying this set of tools in some data sets showed that on the one hand dynamic models are attractive because they allow time-dependent covariates, but on the other hand they may not be appropriate for all data sets since estimation may present restrictions for some of them.
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Alawani, Nadrah. "Structural Characterization of Synthetic Polymers and Copolymers Using Multidimensional Mass Spectrometry Interfaced with Thermal Degradation, Liquid Chromatography and/or Ion Mobility Separation." University of Akron / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=akron1386591497.

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Wang, Xiaohui. "Bayesian classification and survival analysis with curve predictors." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1205.

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Books on the topic "Survival curves"

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Gan, Li. Individual subjective survival curves. Cambridge, Mass: National Bureau of Economic Research, 2003.

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Sloan, David L. The hangover survival guide. Key West, Fla: Phantom Press, 2006.

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Christopher, Shultz, ed. The hangover survival guide. Key West, Fla: Phantom Press, 2006.

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The curve of the world: A novel. Chapel Hill, N.C: Algonquin Books of Chapel Hill, 2002.

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Johnson, Jennifer Elizabeth. The use of 60Co cell survival curves in BNCT research. 1994.

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Elwood, Mark. Chance variation. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682898.003.0008.

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This chapter explains chance variation and statistical tests, including discrete and continuous measures, the concept of significance, one and two sided test, exact tests, precision and confidence limits. It shows tests of differences in proportions and chi-square tests, the Mantel-Haenszel test, and calculation of confidence limits, for simple tables and for stratified data. It covers heterogeneity tests, multiplicative and additive models, ordered exposure variables and tests of trend. It explains statistical tests for matched studies and in multivariate models. Multiple testing, the Bonferroni correction, issues of hypothesis testing and hypothesis generation, and subgroup analyses are discussed. Stopping rules and repeated testing in trials is covered. It explains how to calculate study power and the necessary size of the study. The chapter describes time to event analysis, including survival curves, product-limit and actuarial or life-table methods, and the calculation of confidence limits, relative survival ratios, the log rank test with control for confounding, and multivariate analysis.
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The Curve of the World. Highbridge Audio, 2002.

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Stevens, Marcus. The Curve of the World. Highbridge Audio, 2002.

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The Curve of the World : A Novel. Washington Square Press, 2003.

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Quick, Laura. The Futility Curse as a Northwest Semitic Trope. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198810933.003.0004.

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This chapter considers the possibility that a Northwest Semitic tradition of curses circulated in the Levant in the first millennium, with a description and analysis of examples of the futility curse from the Old Aramaic inscriptions, encompassing the Tell Fakhariyah bilingual inscription, the Sefire Treaties, and the Bukān inscription. This has implications for the reconstruction of a putative Aramaic literary tradition, something often hypothesized but little attested due to the scant material finds which have survived antiquity. Ultimately it is shown that a tradition of cursing expressed in terms of lexical, ideational, and syntactic consistency was shared between these Old Aramaic epigraphs.
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Book chapters on the topic "Survival curves"

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Lindsey, James K. "Survival Curves." In The Analysis of Stochastic Processes using GLIM, 79–102. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2888-2_4.

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Kleinbaum, David G. "Kaplan-Meier Survival Curves and the Log-Rank Test." In Survival Analysis, 45–82. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2555-1_2.

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Giridhar, Prashanth, and Goura K. Rath. "Clinical Significance of Cell Survival Curves." In Practical Radiation Oncology, 171–75. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0073-2_27.

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Palettas, Panickos N., Prem K. Goel, and Purushottam W. Laud. "Bayesian Modeling For Fatigue Crack Curves." In Survival Analysis: State of the Art, 153–70. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7983-4_10.

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Matthews, David Edward, and Vernon Todd Farewell. "6 Kaplan-Meier or ‘Actuarial’ Survival Curves." In Using and Understanding Medical Statistics, 54–66. Basel: KARGER, 2007. http://dx.doi.org/10.1159/000099421.

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Brockett, Patrick L., and Yuxin Zhang. "Actuarial (Mathematical) Modeling of Mortality and Survival Curves." In Handbook of the Mathematics of the Arts and Sciences, 1–33. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-70658-0_69-1.

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Brockett, Patrick L., and Yuxin Zhang. "Actuarial (Mathematical) Modeling of Mortality and Survival Curves." In Handbook of the Mathematics of the Arts and Sciences, 1559–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-57072-3_69.

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Kleinbaum, David G., and Mitchel Klein. "Kaplan-Meier Survival Curves and the Log-Rank Test." In Statistics for Biology and Health, 55–96. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6646-9_2.

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Silva-Fortes, Carina, Maria Antónia Amaral Turkman, Luis Lança, Ricardo Silva, and Gonçalo Marques. "An Application of MRMC ROC Curves on Radiology." In Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications, 445–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34904-1_47.

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Geronikolou, Styliani, and Stelios Zimeras. "SSEv: A New Small Samples Evaluator Based on Modified Survival Curves." In Advances in Experimental Medicine and Biology, 265–70. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57348-9_23.

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Conference papers on the topic "Survival curves"

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Sokota, Samuel, Ryan D'Orazio, Khurram Javed, Humza Haider, and Russell Greiner. "Simultaneous Prediction Intervals for Patient-Specific Survival Curves." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/828.

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Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of models -- known as individual survival distributions (ISDs) -- produces patient-specific survival functions that offer greater descriptive power of patient outcomes than was previously possible. Unfortunately, at the time of writing, ISD models almost universally lack uncertainty quantification. In this paper we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can easily be adapted for patient-specific survival curve analysis and yields accurate results. Furthermore, we introduce both a modification to the existing method and a novel method for estimating simultaneous prediction intervals and show that they offer competitive performance. It is worth emphasizing that these methods are not limited to survival analysis and can be applied in any context in which sampling the distribution of interest is tractable. Code is available at https://github.com/ssokota/spie.
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Chen, Dechang, Huan Wang, Donald E. Henson, Li Sheng, Matthew T. Hueman, and Arnold M. Schwartz. "Clustering Cancer Data by Areas between Survival Curves." In 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). IEEE, 2016. http://dx.doi.org/10.1109/chase.2016.35.

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MOMO, F. R., S. DOYLE, and J. E. URE. "LESLIE MATRICES AND SURVIVAL CURVES CONTAIN THERMODYNAMICAL INFORMATION." In International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814304900_0016.

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Amina, M., V. S. Kodogiannis, E. Z. Panagou, and G. J. E. Nychas. "Modeling the Listeria monocytogenes survival/death curves using wavelet neural networks." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596880.

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Lotsberg, Inge, and Knut O. Ronold. "On the Derivation of Design S-N Curves Based on Limited Fatigue Test Data." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49175.

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Qualification of new characteristic S-N curves for fatigue life assessment of structures is considered to be a significant engineering challenge. First, representative fatigue test data for the actual structural connections have to be derived. Then these test data have to be transferred into characteristic S-N curves that represent a predefined probability of survival. Characteristic S-N curves are also often denoted design S-N curves as these curves are often used directly for fatigue life assessment of structures without application of a material factor. A few large scale tests can add significant confidence to a design S-N curve dependent on the type of structural detail to be designed. The reason for this is that a prototype test specimen can be fabricated in a similar way as the actual connection and it is similar in geometry, material characteristics, residual stress, and fabrication tolerances. In addition it can likely be subjected to a more relevant loading and boundary conditions as compared with that of small scale test specimens. When a limited number of test data are available, it is questioned how a characteristic S-N curve can be derived with a well defined probability of survival. The mentioned issues are further considered in this paper together with some recommendations on how to derive design S-N curves based on limited data.
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Lecornu, L., C. Le Guillou, F. Le Saux, M. Hubert, J. Puentes, and J. M. Cauvin. "ANTEROCOD: Actuarial survival curves applied to medical coding support for chronic diseases." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5627163.

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Schweckendiek, Timo, Mark G. van der Krogt, Ana Teixeira, Wim Kanning, Rob Brinkman, and Katerina Rippi. "Reliability Updating with Survival Information for Dike Slope Stability Using Fragility Curves." In Geo-Risk 2017. Reston, VA: American Society of Civil Engineers, 2017. http://dx.doi.org/10.1061/9780784480700.047.

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Kodogiannis, Vassilis S., and Ilias Petrounias. "Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks." In 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA). IEEE, 2012. http://dx.doi.org/10.1109/cimsa.2012.6269596.

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Ling, Dan, Shun-Peng Zhu, Hong-Zhong Huang, Li-Ping He, and Zhong-Lai Wang. "A Method for Estimating Parameters of P-S-N Curves Based on Weibull Distribution." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47375.

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An S-N curve is a traditional tool for design against fatigue. Because there is often a considerable amount of scatter in fatigue performance of specimens, The P-S-N curves capturing the probability of failure should be employed instead of S-N curves. In order to minimize the time and the number of specimens required for fatigue test, many researches had been done. Most studies were focused on a three-parameter S-N curve model; lognormal distribution and maximum likelihood estimation were employed to estimate unknown parameters. In this paper, a three-parameter Weibull distribution is used to describe the scatter of fatigue life. The relationship among survival probability, stress level and fatigue life is considered. A method for estimating parameters of P-S-N curves is proposed. According to this method, three groups of specimens are needed. Each group is submitted to a stress level. The parameters of P-S-N curves can be estimated by solving a set of nonlinear equations. And a numerical example shows that the method is effective.
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Stewart, David J., Dominick Bosse, Stephanie Brule, Andrew G. Robinson, Michael Ong, and John F. Hilton. "Abstract 1774: Progression-free survival curves suggest a dichotomous determinant of PD-L1 inhibitor efficacy." In Proceedings: AACR Annual Meeting 2017; April 1-5, 2017; Washington, DC. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.am2017-1774.

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Reports on the topic "Survival curves"

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Gan, Li, Michael Hurd, and Daniel McFadden. Individual Subjective Survival Curves. Cambridge, MA: National Bureau of Economic Research, February 2003. http://dx.doi.org/10.3386/w9480.

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Anastasopoulos, Panagiotis, Fred Mannering, and John Haddock. Effectiveness and Service Lives/Survival Curves of Various Pavement Rehabilitation Treatments. West Lafayette, Indiana: Purdue University, 2011. http://dx.doi.org/10.5703/1288284314292.

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Koenker, Roger, and Naveen Narisetty. Censored quantile regression survival models with a cure proportion. The IFS, October 2019. http://dx.doi.org/10.1920/wp.cem.2019.5619.

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