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Dissertations / Theses on the topic 'Survival analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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

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Survival analysis pertains to a statistical approach designed to take into account the amount of time an experimental unit contributes to a study. A Mayo Clinic study of 418 Primary Biliary Cirrhosis patients during a ten year period was used. The Kaplan-Meier Estimator, a non-parametric statistic, and the Cox Proportional Hazard methods were the tools applied. Kaplan-Meier results include total values/censored values.

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2

Abrams, Keith Rowland. "Bayesian survival analysis." Thesis, University of Liverpool, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316744.

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In cancer research the efficacy of a new treatment is often assessed by means of a clinical trial. In such trials the outcome measure of interest is usually time to death from entry into the study. The time to intermediate events may also be of interest, for example time to the spread of the disease to other organs (metastases). Thus, cancer clinical trials can be seen to generate multi-state data, in which patients may be in anyone of a finite number of states at a particular time. The classical analysis of data from cancer clinical trials uses a survival regression model. This type of model

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3

Baade, Ingrid Annette. "Survival analysis diagnostics." Thesis, Queensland University of Technology, 1997.

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4

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

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5

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

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6

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

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7

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

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8

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, s

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9

Salter, Amy Beatrix. "Multivariate dependencies in survival analysis." Title page, contents and introduction only, 1999. http://web4.library.adelaide.edu.au/theses/09PH/09phs177.pdf.

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Bibliography: leaves 177-181. This thesis investigates determinants of factors associated with retention of injecting drug users on the South Australian methadone program over the decade 1981 to mid 1991. Truncated multivariate survival models are proposed for the analysis of data from the program, and the theory of graphical chain models applied to the data. A detailed analysis is presented which gives further insight into the nature of the relationships that exist amongst these data. This provides an application of graphical chain models to survival data.

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10

Wienke, Andreas. "Frailty models in survival analysis." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=985529598.

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11

Zhang, Xinjian. "HIV/AIDS relative survival analysis." unrestricted, 2007. http://etd.gsu.edu/theses/available/etd-07262007-123251/.

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Thesis (M.S.)--Georgia State University, 2007.<br>Title from file title page. Gengsheng (Jeff) Qin, committee chair; Ruiguang (Rick) Song, Xu Zhang, Yu-Sheng Hsu, committee members. Electronic text (79 p. : col. ill.) : digital, PDF file. Description based on contents viewed Sept. 16, 2008. Includes bibliographical references (p. 38-42).

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12

Putcha, Venkata Rama Prasad. "Random effects in survival analysis." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312431.

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13

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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14

Aguilar, Tamara Alejandra Fernandez. "Gaussian processes for survival analysis." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:b5a7a3b2-d1bd-40f1-9b8d-dbb2b9cedd29.

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Survival analysis is an old area of statistics dedicated to the study of time-to-event random variables. Typically, survival data have three important characteristics. First, the response is a waiting time until the occurrence of a predetermined event. Second, the response can be "censored", meaning that we do not observe its actual value but a bound for it. Last, the presence of covariates. While there exists some feasible parametric methods for modelling this type of data, they usually impose very strong assumptions on the real complexity of the response and how it interacts with the covaria

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15

Shani, Najah Turki. "Multivariate analysis and survival analysis with application to company failure." Thesis, Bangor University, 1991. https://research.bangor.ac.uk/portal/en/theses/multivariate-analysis-and-survival-analysis-with-application-to-company-failure(a031bf91-13bc-4367-b4fc-e240ab54a73b).html.

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This thesis offers an explanation of the statistical modelling of corporate financial indicators in the context where the life of a company is terminated. Whilst it is natural for companies to fail or close down, an excess of failure causes a reduction in the activity of the economy as a whole. Therefore, studies on business failure identification leading to models which may provide early warnings of impending financial crisis may make some contribution to improving economic welfare. This study considers a number of bankruptcy prediction models such as multiple discriminant analysis and logit,

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16

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<br>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

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17

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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18

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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19

Aparicio, Vázquez Ignacio. "Venn Prediction for Survival Analysis : Experimenting with Survival Data and Venn Predictors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278823.

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The goal of this work is to expand the knowledge on the field of Venn Prediction employed with Survival Data. Standard Venn Predictors have been used with Random Forests and binary classification tasks. However, they have not been utilised to predict events with Survival Data nor in combination with Random Survival Forests. With the help of a Data Transformation, the survival task is transformed into several binary classification tasks. One key aspect of Venn Prediction are the categories. The standard number of categories is two, one for each class to predict. In this work, the usage of ten c

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20

Müller, Andrea Martina. "Genetic association analysis with survival phenotypes." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-99742.

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21

Aron, Liviu. "Genetic analysis of dopaminergic neuron survival." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-117874.

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22

Mannathoko, Bame Joshua. "Survival analysis of SMMEs in Botswana." Thesis, Nelson Mandela Metropolitan University, 2011. http://hdl.handle.net/10948/1531.

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This study investigates the factors influencing survival of micro enterprises funded by the Department of Youth in Botswana. Data drawn from 271 business ventures established between the years 2005 and 2009 was analysed by using the Cox proportional hazards model (CPHM), a survival analysis technique. Results from the analysis suggest that businesses operated by younger owners endure a higher risk of failure in comparison to businesses owned by older entrepreneurs while firm size at start-up was also a significant determinant of survival. As a component of human capital, a personal contributio

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23

Lu, Xuewen. "Semiparametric regression models in survival analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0030/NQ27458.pdf.

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24

Duchesne, Thierry. "Multiple time scales in survival analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0002/NQ44759.pdf.

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25

Gay, John Michael. "Frailties in the Bayesian survival analysis." Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405009.

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26

Bond, Simon James. "Aspects of competing risks survival analysis." Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/54208/.

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This thesis is focused on the topic of competing risks survival analysis. The first chapter provides an introduction and motivation with a brief literature review. Chapter 2 considers the fundamental functional of all competing risks data: the crude incidence function. This function is considered in the light of the counting process framework which provides powerful mathematics to calculate confidence bands in an analytical form, rather than bootstrapping or simulation. Chapter 3 takes the Peterson bounds and considers what happens in the event of covariate information. Fortunately, these boun

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27

Olivi, Alessandro. "Survival analysis of gas turbine components." Thesis, Linköpings universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129707.

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Survival analysis is applied on mechanical components installed in gas turbines. We use field experience data collected from repair inspection reports. These data are highly censored since the exact time-to-event is unknown. We only know that it lies before or after the repair inspection time. As event we consider irreparability level of the mechanical components. The aim is to estimate survival functions that depend on the different environmental attributes of the sites where the gas turbines operate. Then, the goal is to use this information to obtain optimal time points for preventive maint

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28

Siannis, Fotios. "Sensitivity analysis for correlated survival models." Thesis, University of Warwick, 2001. http://wrap.warwick.ac.uk/78861/.

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In this thesis we introduce a model for informative censoring. We assume that the joint distribution of the failure and the censored times depends on a parameter δ, which is actually a measure of the possible dependence, and a bias function B(t,θ). Knowledge of δ means that the joint distribution is fully specified, while B(t,θ) can be any function of the failure times. Being unable to draw inferences about δ, we perform a sensitivity analysis on the parameters of interest for small values of δ, based on a first order approximation. This will give us an idea of how robust our estimates are in

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29

Alqahtani, Khaled Mubarek A. "Survival analysis based on genomic profiles." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/16471/.

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Accurate survival prediction is critical in the management of cancer patients’ care and well-being. Previous studies have shown that copy number alterations (CNA) in some key genes are individually associated with disease phenotypes and patients’ prognosis. However, in many complex diseases like cancer, it is expected that a large number of genes with such an association span the genome. Furthermore, genome-wide CNA profiles are person-specific. Each patient has their own profile and any differences in the profile between patients may help to explain the differences in the patients’ survival.

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30

Hirst, William Mark. "Outcome measurement error in survival analysis." Thesis, University of Liverpool, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366352.

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31

Glazier, Seth William. "Sequential Survival Analysis with Deep Learning." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7528.

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Survival Analysis is the collection of statistical techniques used to model the time of occurrence, i.e. survival time, of an event of interest such as death, marriage, the lifespan of a consumer product or the onset of a disease. Traditional survival analysis methods rely on assumptions that make it difficult, if not impossible to learn complex non-linear relationships between the covariates and survival time that is inherent in many real world applications. We first demonstrate that a recurrent neural network (RNN) is better suited to model problems with non-linear dependencies in synthetic

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32

Thamrin, Sri Astuti. "Bayesian survival analysis using gene expression." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/62666/1/Sri_Astuti_Thamrin_Thesis.pdf.

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This thesis developed and applied Bayesian models for the analysis of survival data. The gene expression was considered as explanatory variables within the Bayesian survival model which can be considered the new contribution in the analysis of such data. The censoring factor that is inherent of survival data has also been addressed in terms of its impact on the fitting of a finite mixture of Weibull distribution with and without covariates. To investigate this, simulation study were carried out under several censoring percentages. Censoring percentage as high as 80% is acceptable here as the w

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33

Fernandez, Camila. "Contributions and applications to survival analysis." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS230.

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L'analyse de survie a suscité l'intérêt de diverses disciplines, allant de la médecine et de la maintenance prédictive à diverses applications industrielles. Sa popularité croissante peut être attribuée aux avancées significatives en matière de puissance de calcul et à la disponibilité accrue des données. Des approches variées ont été développées pour répondre au défi des données censurées, allant des outils statistiques classiques aux techniques contemporaines d'apprentissage automatique. Cependant, il reste encore une marge considérable pour l'amélioration. Cette thèse vise à introduire des

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34

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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35

Zhang, Zhigang. "Nonproportional hazards regression models for survival analysis /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144473.

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36

Volinsky, Christopher T. "Bayesian model averaging for censored survival models /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8944.

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37

White, Ceri. "Cluster analysis : algorithms, hazards and small area relative survival." Thesis, University of South Wales, 2008. https://pure.southwales.ac.uk/en/studentthesis/cluster-analysis(b799eddf-4d11-4cd2-9cd0-3d0480dcaedd).html.

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This thesis presents research that has demonstrated the use of clustering algorithms in the analysis of datasets routinely collected by cancer registries. This involved a review of existing algorithms and their application in studies of spatial and temporal variations in cancer rates. As a result of continuing public and scientific concern there has been an increase in the numbers of cancer related enquiries in recent years that has helped to raise the profile of the work of cancer registries. There are no official guidelines on the approach to be taken in such studies in relation to cluster a

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38

Zhou, Feifei, and 周飞飞. "Cure models for univariate and multivariate survival data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B45700977.

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39

Parast, Layla. "Landmark Prediction of Survival." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10085.

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The importance of developing personalized risk prediction estimates has become increasingly evident in recent years. In general, patient populations may be heterogenous and represent a mixture of different unknown subtypes of disease. When the source of this heterogeneity and resulting subtypes of disease are unknown, accurate prediction of survival may be difficult. However, in certain disease settings the onset time of an observable intermediate event may be highly associated with these unknown subtypes of disease and thus may be useful in predicting long term survival. Throughout this disse

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40

Oller, Piqué Ramon. "Survival analysis issues with interval-censored data." Doctoral thesis, Universitat Politècnica de Catalunya, 2006. http://hdl.handle.net/10803/6520.

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L'anàlisi de la supervivència s'utilitza en diversos àmbits per tal d'analitzar dades que mesuren el temps transcorregut entre dos successos. També s'anomena anàlisi de la història dels esdeveniments, anàlisi de temps de vida, anàlisi de fiabilitat o anàlisi del temps fins a l'esdeveniment. Una de les dificultats que té aquesta àrea de l'estadística és la presència de dades censurades. El temps de vida d'un individu és censurat quan només és possible mesurar-lo de manera parcial o inexacta. Hi ha diverses circumstàncies que donen lloc a diversos tipus de censura. La censura en un interval fa r

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41

Kriner, Monika. "Survival Analysis with Multivariate adaptive Regression Splines." Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-73695.

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42

Villeneuve, Paul. "Population based survival analysis of childhood cancer." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/10442.

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Purpose: To assess the survival of children diagnosed with cancer between 1982 and 1988 using population based data. Subjects: 4715 cancer patients diagnosed with cancer prior to age twenty, between 1982-1988, as reported population based cancer registries. Mortality status (up to December 31, 1991) was ascertained by linking subjects to the Canadian mortality database. Actuarial survival rates and assessment of the role of covariates (ie. gender, age at diagnosis, year of diagnosis) on survival using the proportional hazards model. The five year survival rate of children diagnosed with a prim

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43

Kakuma, Ritsuko. "Delirium in the elderly : a survival analysis." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33414.

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Mortality rates have consistently been shown to be greater in patients with delirium compared to those without. Published work over the last decade has revealed however, that several confounding factors play key roles in contributing to the excess mortality in the delirium population and that statistical adjustment for these factors in multivariate analyses minimizes, if not eliminates, the association between delirium and mortality. These factors include pre-existing dementia, advanced age, severe medical illness, diminished functional status, and intoxication or withdrawal from medications.

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Xiao, Yongling. "Flexible marginal structural models for survival analysis." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107571.

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In longitudinal studies, both treatments and covariates may vary throughout the follow-up period. Time-dependent (TD) Cox proportional hazards (PH) models can be used to model the effect of time-varying treatments on the hazard. However, two challenges exist in such modeling. First, accurate modeling of the effects of TD treatments on the hazard requires resolving the uncertainty about the etiological relevance of treatments taken in different time periods. The second challenge arises in the presence of TD confounders affected by prior treatments. By assuming the absence of the other challenge

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45

Long, Yongxian, and 龙泳先. "Semiparametric analysis of interval censored survival data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45541152.

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46

McKinley, Trevelyan John. "Spatial survival analysis of infectious animal diseases." Thesis, University of Exeter, 2007. http://hdl.handle.net/10036/27033.

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This thesis investigates the feasibility of using spatial survival modelling techniques to develop dynamic space-time predictive models of risk for infectious animal disease epidemics. Examples of diseases with potentially vast socioeconomic impacts include avian influenza, bovine tuberculosis and foot-and-mouth disease (FMD), all of which have received wide coverage in the recent media. The relatively sporadic occurrence of such large scale animal disease outbreaks makes determination of optimal control policies difficult, and policy makers must balance the relative impacts of different respo

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47

Freeman, S. N. "Statistical analysis of avian breeding and survival." Thesis, University of Kent, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.278099.

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Anzures-Cabrera, Judith. "Survival analysis : competing risks, truncation and immunes." Thesis, University of Warwick, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413431.

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49

Nieto-Barajas, Luis E. "Bayesian nonparametric survival analysis via Markov processes." Thesis, University of Bath, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343767.

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50

Saul, Alan D. "Gaussian process based approaches for survival analysis." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/17946/.

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Traditional machine learning focuses on the situation where a fixed number of features are available for each data-point. For medical applications each individual patient will typically have a different set of clinical tests associated with them. This results in a varying number of observed per patient features. An important indicator of interest in medical domains is survival information. Survival data presents its own particular challenges such as censoring. The aim of this thesis is to explore how machine learning ideas can be transferred to the domain of clinical data analysis. We consider

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