Relevant bibliographies by topics / Survival data analysi

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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 data analysi'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Survival data analysi"

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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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N.Sundaram, N. Sundaram, and P. Venkatesan P.Venkatesan. "Modeling of Parametric Bayesian Cure Rate Survival for Pulmonary Tuberculosis Data Analysis." International Journal of Scientific Research 3, no. 6 (June 1, 2012): 35–49. http://dx.doi.org/10.15373/22778179/june2014/171.

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V. Vallinayagam, V. Vallinayagam, S. Parthasarathy S. Parthasarathy, and P. Venkatesan P. Venkatesan. "A Comparative Study of Life Time Models in the Analysis of Survival Data." Indian Journal of Applied Research 4, no. 1 (October 1, 2011): 344–47. http://dx.doi.org/10.15373/2249555x/jan2014/101.

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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 (December 25, 2017): 303–12. http://dx.doi.org/10.15407/jnpae2017.04.303.

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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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Jayet, H., and A. Moreau. "Analysis of survival data." Journal of Econometrics 48, no. 1-2 (April 1991): 263–85. http://dx.doi.org/10.1016/0304-4076(91)90041-b.

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Lagakos, S. "Analysis of survival data." Controlled Clinical Trials 7, no. 1 (March 1986): 85. http://dx.doi.org/10.1016/0197-2456(86)90009-7.

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Schoenfeld, David, D. R. Cox, and D. Oakes. "Analysis of Survival Data." Journal of the American Statistical Association 81, no. 394 (June 1986): 572. http://dx.doi.org/10.2307/2289259.

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Kenyon, James R. "Analysis of Multivariate Survival Data." Technometrics 44, no. 1 (February 2002): 86–87. http://dx.doi.org/10.1198/tech.2002.s658.

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Dissertations / Theses on the topic "Survival data analysi"

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LIU, XIAOQIU. "Managing Cardiovascular Risk in Hypertension: Methodological Issues in Blood Pressure Data Analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/154475.

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Hypertension remains in 2017 a leading cause of mortality and disability worldwide. A number of issues related to the determinants of cardiovascular risk in hypertensive patients and to the strategies for better hypertension control are still pending. In such a context, aims of my research program were: 1. To investigate the contribution of blood pressure variability to the risk of cardiovascular mortality in hypertensive patients. In this setting, different methods for assessing blood pressure variability and different models exploring the link between blood pressure variability and outcome were investigated. 2. To assess the possibility that a hypertension management strategy based on hemodynamic assessment of patients through impedance cardiography might lead to a better hypertension control over 24 hours than a conventional approach only based on blood pressure measurement during clinic visits. To these aims, this thesis summarizes data obtained by performing a). An in-depth analysis of a study conducted in the Dublin hypertensive population, including 11492 subjects, and b). The analysis of longitudinal data collected in the frame of BEAUTY (BEtter control of blood pressure in hypertensive pAtients monitored Using the hoTman® sYstem) study. In Dublin study, the proportional hazard Cox model and accelerated failure time models have been used to estimate the additional effect of blood pressure variability on cardiovascular mortality over and above the effect of increased mean BP levels, with an attempt to identify the best threshold values for risk stratification. On the other hand, in BEAUTY study, mixed model and generalized estimation equation are used for the longitudinal data analysis.
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TASSISTRO, ELENA. "Adverse events in survival data: from clinical questions to methods for statistical analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/365520.

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Nello studio di un nuovo trattamento con un tempo di sopravvivenza come outcome, l’insuccesso può essere definito in modo da includere un evento avverso serio (AE) tra gli endpoint tipicamente considerati, come ad esempio ricaduta o progressione. Questi eventi si comportano come rischi competitivi, dove l’occorrenza di una ricaduta come primo evento e il conseguente cambio di trattamento escludono la possibilità di osservare AE legati al trattamento stesso. L’analisi degli AE può essere affrontata mediante due diversi approcci: 1. descrizione dell’occorrenza osservata di AE come primo evento: la capacità del trattamento di proteggere dalla ricaduta ha un impatto sulla possibilità di osservare AE dovuti all’azione dei rischi competitivi. 2. Valutazione dell’impatto del trattamento sullo sviluppo di AE in pazienti che sono liberi da ricaduta nel tempo: si dovrebbe considerare l’occorrenza di AE come se la ricaduta non escludesse la possibilità di osservare AE legati al trattamento stesso. Nella prima parte della tesi abbiamo rivisto la strategia di analisi per i due approcci partendo dal tipo di domanda clinica di interesse. Quindi abbiamo identificato le quantità più adatte e i possibili stimatori (proporzione grezza, tasso di AE, incidenza grezza, stimatori smoothed di Kaplan-Meier e di Aalen-Nelson per l’hazard causa-specifico) e li abbiamo valutati relativamente a due aspetti, solitamente necessari in un contesto di sopravvivenza: (i) Lo stimatore dovrebbe tenere in considerazione la presenza di censura a destra (ii) La quantità teorica e lo stimatore dovrebbero essere funzioni del tempo. Nella seconda parte della tesi abbiamo proposto metodi alternativi, come modelli di regressione, curve di Kaplan-Meier stratificate e inverse probability of censoring weighting, per rilassare l’assunto di indipendenza tra i tempi potenziali di AE e di ricaduta. Abbiamo mostrato attraverso simulazioni che questi metodi superano i problemi legati all’uso dei classici stimatori per i rischi competitivi nel secondo approccio. In particolare, abbiamo simulato differenti scenari fissando l’hazard di ricaduta indipendente da due covariate binarie, dipendente da X1, dipendente da entrambe le covariate X1 e X2 anche attraverso la loro interazione. Abbiamo mostrato che si può gestire la selezione dei pazienti, e quindi ottenere indipendenza condizionata tra i tempi potenziali, aggiustando per tutte le covariate osservate. Si noti che anche aggiustando solo per poche covariate osservate come nella realtà a causa di covariate non misurate, si ottengono stime meno distorte rispetto a quelle che si ottengono dal Kaplan-Meier naive censurando per la ricaduta. Infatti, abbiamo dimostrato che la stima ottenuta con il Kaplan-Meier naive è sempre distorta a meno che l’hazard di ricaduta sia indipendente dalle covariate. In un ipotetico scenario dove tutte le covariate sono osservate, la stima della sopravvivenza media pesata ottenuta sia non parametricamente sia dal modello di Cox e la stima della sopravvivenza dall’inverse probability of censoring weighting dovrebbero essere non distorte (metodi applicati aggiustando per entrambe le covariate). Inoltre, segnaliamo che con l’inverse probability of censoring weighting si possono ottenere stime distorte quando tutte le possibili interazioni tra le covariate osservate non sono incluse nel modello per stimare i pesi. Tuttavia, l’inserimento dell’interazione non è necessario quando si usa il modello di Cox pesato, poiché condizionatamente alle covariate osservate, questo modello è robusto nella stima della sopravvivenza media. Ciò nonostante, una limitazione nell’uso del metodo della sopravvivenza media pesata è dato dal fatto che può essere utilizzato solo in presenza di covariate binarie (o categoriche), poiché se la covariata è continua non è possibile identificare i sottogruppi entro cui la funzione di sopravvivenza è stimata.
When studying a novel treatment with a survival time outcome, failure can be defined to include a serious adverse event (AE) among the endpoints typically considered, for instance relapse or progression. These events act as competing risks, where the occurrence of relapse as first event and the subsequent treatment change exclude the possibility of observing AE related to the treatment itself. In principle, the analysis of AE could be tackled by two different approaches: 1. the description of the observed occurrence of AE as first event: treatment ability to protect from relapse has an impact on the chance of observing AE due to the competing risks action. 2. the assessment of the treatment impact on the development of AE in patients who are relapse free in time: one should consider the occurrence of AE as if relapse would not exclude the possibility of observing AE related to the treatment itself. In the first part of the thesis we reviewed the strategy of analysis for the two approaches starting from the type of clinical question of interest. Then we identified the suitable quantities and possible estimators (crude proportion, AE rate, crude incidence, Kaplan-Meier and Aalen-Nelson smoothed estimators of the cause-specific hazard) and judge them according to two features, usually needed in a survival context: (i) the estimator should address for the presence of right censoring (ii) the theoretical quantity and estimator should be functions of time. In the second part of the thesis we proposed alternative methods, such as regression models, stratified Kaplan-Meier curves and inverse probability of censoring weighting, to relax the assumption of independence between the potential time to AE and the potential time to relapse. We showed through simulations that these methods overcome the problems related to the use of standard competing risks estimators in the second approach. In particular, we simulated different scenarios setting the hazard of relapse independent from two binary covariates, dependent from X1 only, dependent from both covariates X1 and X2, also through their interaction. We showed that one can handle patients’ selection, and thus obtain conditional independence between the two potential times, adjusting for all the observed covariates. Of note, even adjusting only for few observed covariates as in the reality due to unmeasured covariates, gives less biased estimates with respect to the estimate obtained from the naive Kaplan-Meier censoring by relapse. In fact, we proved that the estimate obtained from the naive Kaplan-Meier is always biased unless the hazard of relapse is independent from the covariates values. In an hypothetical scenario where all the covariates are observed, the weighted average survival estimate obtained either non parametrically or by the Cox model and the survival estimate from the inverse probability of censoring weighting would be unbiased (methods applied adjusting for both covariates). In addition, we point out that with the inverse probability of censoring weighting method one could obtained biased estimates when all the possible interactions between the observed covariates are not included in the model to estimate the weights. However, the inclusion of the interaction is not needed when the weighted Cox model is used, since conditional on the observed covariates, this model is robust in estimating the average survival. Nevertheless, a limitation in the use of the weighted average survival method is given by the fact that it may be applied only in the presence of binary (or categorical) covariates, since if the covariate is continuous it is impossible to identify the subgroups in which the survival function is estimated.
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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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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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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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Ip, Ying-Kit David. "Analysis of clustered grouped survival data /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2353011x.

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Lee, Yau-wing. "Modelling multivariate survival data using semiparametric models." Click to view the E-thesis via HKUTO, 2000. http://sunzi.lib.hku.hk/hkuto/record/B4257528X.

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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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Kulich, Michal. "Additive hazards regression with incomplete covariate data /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9562.

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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 analysi"

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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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Analysis of multivariate survival data. New York: Springer, 2000.

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

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1961-, Chen Ming-Hui, and Sinha Debajyoti, eds. Bayesian survival analysis. New York: Springer, 2001.

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Statistical methods for survival data analysis. 2nd ed. New York: Wiley, 1992.

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

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Murphy, Roberta. Survival analysis of nurse manpower data. [S.l: The Author], 1997.

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Book chapters on the topic "Survival data analysi"

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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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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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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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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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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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Bhattacharjee, Atanu. "Survival Analysis." In Big Data Analytics in Oncology with R, 1–24. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003185598-1.

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Everitt, Brian, and Sophia Rabe-Hesketh. "Survival Analysis I." In Analyzing Medical Data Using S-PLUS, 345–59. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3285-6_17.

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Ibrahim, Joseph G., Ming-Hui Chen, and Debajyoti Sinha. "Joint Models for Longitudinal and Survival Data." In Bayesian Survival Analysis, 262–89. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3447-8_7.

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Bhattacharjee, Atanu. "Parametric Survival Analysis." In Big Data Analytics in Oncology with R, 39–48. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003185598-3.

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Shoukri, Mohamed M. "Analysis of Survival Data." In Analysis of Correlated Data with SAS and R, 335–82. Fourth edition. | Boca Raton : CRC Press, 2018. | Previous edition: Analysis of correlated data with SAS and R / Mohamed M. Shoukri (Boca Raton : Chapman & Hall/CRC, 2007).: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315277738-9.

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Conference papers on the topic "Survival data analysi"

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Mosquéra, Júlia Milhomem, Amanda Ribeiro Alves, Gianna Carolina Pereira Cavalli, Larissa Feitosa de Albuquerque Lima Ramos, and Flávio Lúcio Vasconcelos. "GLOBAL SURVIVAL BASED ON CLINICAL, HISTOLOGICAL, AND BIOLOGICAL TUMOR CRITERIA IN A SECONDARY PUBLIC BRAZILIAN HOSPITAL." In Abstracts from the Brazilian Breast Cancer Symposium - BBCS 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s2045.

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Objective: To analyze the overall survival of women with breast cancer based on clinical, histological, and biological tumor data in a secondary hospital in Federal District/Brazil. Method: Retrospective cohort study of women diagnosed with breast cancer from 2012 to 2019, followed up until December 2020, having its data analyzed in 2021. The population studied was from the area covered of the Regional Hospital of Santa Maria (Brasília/Distrito Federal/Brazil), a secondary service, linked to the Brazilian Unified Health System. The information analyzed in this study were state at the last visit (life or dead), the presence of clinically compromised axillary lymph nodes, staging by the TNM system, location of distant metastasis (bone or visceral), histological type and grade, and tumor biological profile. Subsequently, survivals were analyzed in relation to variables previously described. The data were analyzed with the aid of the statistical package SPSS (version 26.0), with p<0.05 is considered significant. Results: This study included a total of 203 patients, of which 158 (77.8%) survived and 45 (22.2%) died. Regarding deaths, 67.5% had a clinically compromised armpit (p<0.001) and 50% were in stage IV (p<0.001). In relation to overall survival, worse survival was observed for patients with clinically suspect lymph nodes (p<0.001), for tumors measuring between 2 and 5 cm and tumors larger than 5 cm in relation to tumors smaller than 2 cm (p<0.001), and for stages III and IV compared to stages I and II (p<0.001). There was no worsening of survival in relation to the histological type (p=0.39), histological grade (p=0.65), location of metastases (bone and visceral) (p=0.76), or biological profile (p=0.40). Conclusion: There were more deaths in relation to the clinically compromised axillary state and in stages III and IV. Larger tumors, more advanced staging, and a clinically compromised armpit worsened overall survival.
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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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Bagkavos, Dimitrios I., Aglaia Kalamatianou, and Dimitrios Ioannides. "Kernel based confidence intervals for survival function estimation." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0040.

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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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Yang, Wanshan, Ting Huang, Junlin Zeng, Yan Tang, Lijun Chen, Shivakant Mishra, and Youjian Eugene Liu. "Purchase Prediction in Free Online Games via Survival Analysis." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006031.

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Yadav, Pranjul, Michael Steinbach, Lisiane Pruinelli, Bonnie Westra, Connie Delaney, Vipin Kumar, and Gyorgy Simon. "Forensic Style Analysis with Survival Trajectories." In 2015 IEEE International Conference on Data Mining (ICDM). IEEE, 2015. http://dx.doi.org/10.1109/icdm.2015.152.

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Li, Yan, Kevin S. Xu, and Chandan K. Reddy. "Regularized Parametric Regression for High-dimensional Survival Analysis." In Proceedings of the 2016 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2016. http://dx.doi.org/10.1137/1.9781611974348.86.

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Moat, Genevieve, and Shirley Coleman. "Survival Analysis and Predictive Maintenance Models for non-sensored Assets in Facilities Management." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671625.

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Rahman, Md Mahmudur, and Sanjay Purushotham. "Fair and Interpretable Models for Survival Analysis." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3539259.

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Reports on the topic "Survival data analysi"

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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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Zhang, Dan, Jingting Liu, Mengxia zheng, Chunyan Meng, and Jianhua Liao. Prognostic and Clinicopathological significance of CD155 Expression in Cancer Patients: A Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0087.

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Review question / Objective: The present study aimed to comprehensively explore the relationship between CD155 expression and clinical characteristics and prognosis of cancer patients. The study was based on comprehensive search of relevant literature. In particular, the study attempted to define the role of CD155 in various cancer types. Eligibility criteria: The pre-established inclusion criteria were as follows: (1) all subjects were cancer patients who received standard treatment; (2) The expression of CD155 in the cancer patients was well-examined, and all patients were assigned into two groups based on the expression; (3) survival analysis was performed based on these two groups, and provided sufficient data to estimate the risk ratio (HR) and 95% confidence interval (CI) for overall survival (OS); and (4) scientific and reasonable research. Case reports, reviews, abstracts, letters, bioinformatic analysis, TCGA analysis, and articles that did not meet the inclusion criteria were excluded from analyses.
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Zhu, Yi-Bing, Yan Yao, Yuan Xu, and Hui-Bin Huang. Nitrogen balance and Outcomes in Critically Ill Patients: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0134.

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Review question / Objective: This study aimed to evaluate the impact of Nitrogen balance (NB) on prognosis in such a patient population. Condition being studied: Nitrogen balance and Outcomes in Critically Ill Patients. Eligibility criteria: 1) The study focused on the association between NB level and the mortality risk in adult (≥18 years old) patients; 2) The outcome data included any reporting form of survival data that could be extracted; and 3) The study design was limited to cohort, case-control, or RCT design.
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Chen, Xiaole, Peng Wang, Yunquan Luo, Yi-Yu Lu, Wenjun Zhou, Mengdie Yang, Jian Chen, Zhi-Qiang Meng, and Shi-Bing Su. Therapeutic Efficacy Evaluation and Underlying Mechanisms Prediction of Jianpi Liqi Decoction for Hepatocellular Carcinoma. Science Repository, September 2021. http://dx.doi.org/10.31487/j.jso.2021.02.04.sup.

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Objective: The aim of this study was to assess the therapeutic effects of Jianpi Liqi decoction (JPLQD) in hepatocellular carcinoma (HCC) and explore its underlying mechanisms. Methods: The characteristics and outcomes of HCC patients with intermediate stage B who underwent sequential conventional transcatheter arterial chemoembolization (cTACE) and radiofrequency ablation (RFA) only or in conjunction with JPLQD were analysed retrospectively. The plasma proteins were screened using label-free quantitative proteomics analysis. The effective mechanisms of JPLQD were predicted through network pharmacology approach and partially verified by ELISA. Results: Clinical research demonstrated that the Karnofsky Performance Status (KPS), traditional Chinese medicine (TCM) syndrome scores, neutropenia and bilirubin, median progression-free survival (PFS), and median overall survival (OS) in HCC patients treated with JPLQD were superior to those in patients not treated with JPLQD (all P<0.05). The analysis of network pharmacology, combined with proteomics, suggested that 52 compounds targeted 80 potential targets, which were involved in the regulation of multiple signaling pathways, especially affecting the apoptosis-related pathways including TNF, p53, PI3K-AKT, and MAPK. Plasma IGFBP3 and CA2 were significantly up-regulated in HCC patients with sequential cTACE and RFA therapy treated with JPLQD than those in patients not treated with JPLQD (P<0.001). The AUC of the IGFBP3 and CA2 panel, estimated using ROC analysis for JPLQD efficacy evaluation, was 0.867. Conclusion: These data suggested that JPLQD improves the quality of life, prolongs the overall survival, protects liver function in HCC patients, and exhibits an anticancer activity against HCC. IGFBP3 and CA2 panels may be potential therapeutic targets and indicators in the efficacy evaluation for JPLQD treatment, and the effective mechanisms involved in the regulation of multiple signaling pathways, possibly affected the regulation of apoptosis.
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Zhao, Binghao, Yu Wang, and Wenbin Ma. Comparative Efficacy and Safety of Therapeutics for Elderly Glioblastoma: a Bayesian Network Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0094.

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Review question / Objective: At this time, a comprehensive systematic review and network meta-analysis (NMA) was conducted to: (1) fill the research gap by giving rankings on treatment efficacy; (2) provide statistical evidence of not head-to-head comparisons; (3) seek out the best and up-to-date therapeutic strategy reported in latest RCTs; (4) address potential adverse events (AEs) of available treatments. Condition being studied: The incidence of glioblastoma (GBM) increases with age, until now, there has been less evidence on the optimal treatments for elderly GBM since only general GBM populations were included in clinical trials. Given the poor survival of elderly GBM, we collected randomized controlled trials about newly diagnosed GBM (ndGBM) and recurrent GBM, and conducted a Bayesian network meta-analysis on ndGBM regarding overall survival (OS) and progression-free survival (PFS). We revealed TTF + TMZ and TMZ + HFRT were likely to be best treatments for OS; BEV + HFRT and TMZ + HFRT were likely to be best options for PFS. Current study is the most comprehensive and powered network analysis on elderly GBM until now, it also provides more insights for elderly GBM management.
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Kuo, Meng-Hsuan, Chih-Wei Tseng, Ching-Sheng Hsu, Yen-Chun Chen, I.-Ting Kao, and Chen-Yi Wu. Protocol for systematic review and meta-analysis of prognostic value of sarcopenia in advanced HCC patients treating with systemic therapy. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0011.

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Review question / Objective: P: Advanced HCC patients under systemic therapy; I: low skeletal muscle mass (LSMM); C: Non-LSMM; O:overall survival or mortality. Eligibility criteria: (1) cohort studies or cross sectional studies investigations with HCC patients treated with systemic therapy; (2) the articles estimated pretreatment skeletal muscle mass measured by CT-images; (3) studies provided statistical data about the prevalence pretreatment LSMM or influence of LSMM on OS orPFS.
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Zheng, Jiaxi, and Haihua Yang. Clinical Benefits of Immune Checkpoint Inhibitors and Predictive Value of Tumor Mutation Burden in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0008.

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Review question / Objective: Is immunotherapy associated with beneficial clinical outcomes for hepatocellular carcinoma (HCC) and how can combination immunotherapy be deployed to produce the best benefit? Is tumor mutation burden (TMB) a predictive biomarker for immune‐checkpoint inhibitors? Condition being studied: To this date, about 50 single-arm clinical trials and several randomized control trials (RCTs) presented final or interim results of investigations on the efficacy of PD-1/PD-L1 inhibitors for advanced HCC. In the CheckMate 459, IMbrave 050, and ORIENT-32, immunotherapies were found to significantly improve progression-free survival (PFS) and overall survival (OS) compared with sorafenib (a tyrosine-kinase inhibitor, as standard systemic treatment) in patients with advanced hepatocellular carcinoma. However, these clinical trials were different on clinical phases, sample size, and response evaluation criteria, and inconsistent clinical outcomes were shown in several trials.
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Wang, Qing, Zi-Xu Wang, Nasu M. Otomi, and Shinji Mine. Association between cutoffs for classifying high- and low-volume hospitals and long-term survival after eophagectomy: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2022. http://dx.doi.org/10.37766/inplasy2022.7.0023.

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Review question / Objective: It is still unclear about the association between cutoffs for classifying high- and low-volume hospitals and long-term survival after esophagectomy for patients with esophageal cancer. Condition being studied: It remains controversial whether size of hospital volume influences long-term survival outcomes for patients with esophageal cancer after esophagectomy. In addition, there is still no consensus for defining a reasonable cutoff of esophagectomies per year for classifying high- and low-volume hospitals. Information sources: After the retrieval of the relevant articles, they were screened to remove the duplicates. Search results were screened by two authors (Q.W. and Z.X.W.) independently according to the titles and abstracts. Next, the retained studies were searched for their full text and further were screened according to the following criteria: surgery for esophageal carcinoma as the theme; primary outcomes included hospital volume and long-term OS; comparison of OS between high- and low-volume hospitals; original articles with informative data; articles reporting adjusted hazard ratios (HRs) in multi-variate analysis; and articles in which procedural volume was an exact cutoff. Any disagreements were resolved through consultation with the third author.
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