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Crumer, Angela Maria. "Comparison between Weibull and Cox proportional hazards models." Kansas State University, 2011. http://hdl.handle.net/2097/8787.
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Master of ScienceDepartment of Statistics
James J. Higgins
The time for an event to take place in an individual is called a survival time. Examples include the time that an individual survives after being diagnosed with a terminal illness or the time that an electronic component functions before failing. A popular parametric model for this type of data is the Weibull model, which is a flexible model that allows for the inclusion of covariates of the survival times. If distributional assumptions are not met or cannot be verified, researchers may turn to the semi-parametric Cox proportional hazards model. This model also allows for the inclusion of covariates of survival times but with less restrictive assumptions. This report compares estimates of the slope of the covariate in the proportional hazards model using the parametric Weibull model and the semi-parametric Cox proportional hazards model to estimate the slope. Properties of these models are discussed in Chapter 1. Numerical examples and a comparison of the mean square errors of the estimates of the slope of the covariate for various sample sizes and for uncensored and censored data are discussed in Chapter 2. When the shape parameter is known, the Weibull model far out performs the Cox proportional hazards model, but when the shape parameter is unknown, the Cox proportional hazards model and the Weibull model give comparable results.
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Meier, Amalia Sophia. "Discrete proportional hazards models for uncertain outcomes /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/9579.
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Betton, Sandra Ann. "Bankruptcy : a proportional hazard approach." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26056.
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The recent dramatic increase in the corporate bankruptcy rate, coupled with a similar rate of increase in the bank failure rate, has re-awakened investor, lender and government interest in the area of bankruptcy prediction. Bankruptcy prediction models are of particular value to a firm's current and future creditors who often do not have the benefit of an actively traded market in the firm's securities from which to make inferences about the debtor's viability. The models commonly used by many experts in an endeavour to predict the possibility of disaster are outlined in this paper.
The proportional hazard model, pioneered by Cox [1972], assumes that the hazard function, the risk of failure, given failure has not already occurred, is a function of various explanatory variables and estimated coefficients multiplied by an arbitrary and unknown function of time. The Cox Proportional Hazard model is usually applied in medical studies; but, has recently been applied to the bank failure question [Lane, Looney & Wansley, 1986]. The model performed well in the narrowly defined, highly regulated, banking industry. The principal advantage of this approach is that the model incorporates both the survival times observed and any censoring of data thereby using more of the available information in the analysis. Unlike many bankruptcy prediction models, such as logit and probit based regression models, the Cox model estimates the probability distribution of survival times. The proportional hazard model would, therefore, appear to offer a useful addition to the more traditional bankruptcy prediction models mentioned above.
This paper evaluates the applicability of the Cox proportional hazard model in the more diverse industrial environment. In order to test this model, a sample of 109 firms was selected from the Compustat Industrial and Research Industrial data tapes. Forty one of these firms filed petitions under the various bankruptcy acts applicable between 1972 and 1985 and were matched to 67 firms which had not filed petitions for bankruptcy during the same period. In view of the dramatic changes in the bankruptcy regulatory environment caused by the Bankruptcy reform act of 1978, the legal framework of the bankruptcy process was also examined.
The performance of the estimated Cox model was then evaluated by comparing its classification and descriptive capabilities to those of an estimated discriminant analysis based model. The results of this study indicate that while the classification capability of the Cox model was less than that of discriminant analysis, the model provides additional information beyond that available from the discriminant analysis.Business, Sauder School of
Graduate
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Ishak, Khajak. "Omitting a strong covariate from Cox's proportional hazards model." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33005.
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The primary objective of this thesis is to explore the effect of omitting a strong prognostic factor from Cox's proportional hazards model when analyzing data from randomized trials. The secondary objective is to provide an overview of the Cox model. We first present the properties of the model, the method of maximum partial likelihood and the elements available on which to draw inferences concerning parameters. We then discuss various methods used to assess the tenability of the proportional hazards assumption as well as ways to incorporate non-proportional hazards in the model.In the third and final chapter, we address the primary objective of the thesis. In linear regression analysis, unbiased estimates of the effect of the intervention can be obtained even when important but balanced determinants of the outcome are omitted from the model; the precision of the estimates are improved, however, with the inclusion of strong covariates. The logistic and Cox regression (and other non-linear) models do not share this property, however. We discuss the literature on this topic and provide examples to illustrate the problem. We examine the situation for the Cox model in more detail with the analysis of data from an experiment on the effect of increased sexual activity on the longevity of male fruitflies.
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Mair, Patrick, and Marcus Hudec. "Session Clustering Using Mixtures of Proportional Hazards Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/598/1/document.pdf.
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Emanating from classical Weibull mixture models we propose a framework for clustering survival data with various proportionality restrictions imposed. By introducing mixtures of Weibull proportional hazards models on a multivariate data set a parametric cluster approach based on the EM-algorithm is carried out. The problem of non-response in the data is considered. The application example is a real life data set stemming from the analysis of a world-wide operating eCommerce application. Sessions are clustered due to the dwell times a user spends on certain page-areas. The solution allows for the interpretation of the navigation behavior in terms of survival and hazard functions. A software implementation by means of an R package is provided. (author´s abstract)Series: Research Report Series / Department of Statistics and Mathematics
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Sarker, Md Shah Jalal. "Tests for Weibull based proportional hazards frailty models." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/1046/.
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Oman, J. P. "Case influence in proportional hazards with an application in renal transplantation." Thesis, University of Newcastle Upon Tyne, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287419.
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He, Bin. "APPLICATION OF THE EMPIRICAL LIKELIHOOD METHOD IN PROPORTIONAL HAZARDS MODEL." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4384.
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In survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of the model assumptions is a key component. However, the presence of complicated types of censoring such as double censoring and partly interval-censoring in survival data makes model assessment difficult, and the existing tests for goodness-of-fit do not have direct extension to these complicated types of censored data. In this work, we use empirical likelihood (Owen, 1988) approach to construct goodness-of-fit test and provide estimates for the Cox model with various types of censored data. Specifically, the problems under consideration are the two-sample Cox model and stratified Cox model with right censored data, doubly censored data and partly interval-censored data. Related computational issues are discussed, and some simulation results are presented. The procedures developed in the work are applied to several real data sets with some discussion.Ph.D.
Department of Mathematics
Sciences
Mathematics
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Li, Qiuju. "Statistical inference for joint modelling of longitudinal and survival data." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/statistical-inference-for-joint-modelling-of-longitudinal-and-survival-data(65e644f3-d26f-47c0-bbe1-a51d01ddc1b9).html.
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In longitudinal studies, data collected within a subject or cluster are somewhat correlated by their very nature and special cares are needed to account for such correlation in the analysis of data. Under the framework of longitudinal studies, three topics are being discussed in this thesis. In chapter 2, the joint modelling of multivariate longitudinal process consisting of different types of outcomes are discussed. In the large cohort study of UK north Stafforshire osteoarthritis project, longitudinal trivariate outcomes of continuous, binary and ordinary data are observed at baseline, year 3 and year 6. Instead of analysing each process separately, joint modelling is proposed for the trivariate outcomes to account for the inherent association by introducing random effects and the covariance matrix G. The influence of covariance matrix G on statistical inference of fixed-effects parameters has been investigated within the Bayesian framework. The study shows that by joint modelling the multivariate longitudinal process, it can reduce the bias and provide with more reliable results than it does by modelling each process separately. Together with the longitudinal measurements taken intermittently, a counting process of events in time is often being observed as well during a longitudinal study. It is of interest to investigate the relationship between time to event and longitudinal process, on the other hand, measurements taken for the longitudinal process may be potentially truncated by the terminated events, such as death. Thus, it may be crucial to jointly model the survival and longitudinal data. It is popular to propose linear mixed-effects models for the longitudinal process of continuous outcomes and Cox regression model for survival data to characterize the relationship between time to event and longitudinal process, and some standard assumptions have been made. In chapter 3, we try to investigate the influence on statistical inference for survival data when the assumption of mutual independence on random error of linear mixed-effects models of longitudinal process has been violated. And the study is conducted by utilising conditional score estimation approach, which provides with robust estimators and shares computational advantage. Generalised sufficient statistic of random effects is proposed to account for the correlation remaining among the random error, which is characterized by the data-driven method of modified Cholesky decomposition. The simulation study shows that, by doing so, it can provide with nearly unbiased estimation and efficient statistical inference as well. In chapter 4, it is trying to account for both the current and past information of longitudinal process into the survival models of joint modelling. In the last 15 to 20 years, it has been popular or even standard to assume that longitudinal process affects the counting process of events in time only through the current value, which, however, is not necessary to be true all the time, as recognised by the investigators in more recent studies. An integral over the trajectory of longitudinal process, along with a weighted curve, is proposed to account for both the current and past information to improve inference and reduce the under estimation of effects of longitudinal process on the risk hazards. A plausible approach of statistical inference for the proposed models has been proposed in the chapter, along with real data analysis and simulation study.
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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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Milner, A. D. "Detecting changes in covariate effect in the Cox proportional hazards model." Thesis, University of Newcastle Upon Tyne, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239639.
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Chapman, Joanne Shirley. "Statistical methods for gamma mixtures of proportional hazards survival models." Thesis, Lancaster University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340567.
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Alshanbari, Huda Mohammed H. "Additive Cox proportional hazards models for next-generation sequencing data." Thesis, University of Leeds, 2017. http://etheses.whiterose.ac.uk/19739/.
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Eighty-Nine Non-Small Cell Lung Cancer (NSCLC) patients experience chromosomal rearrangements called Copy Number Alteration (CNA), where the cells have abnormal number of copies in one or more regions in their genome, this genetic alteration are known to drive cancer development. An important aim of this thesis is to propose a way to combine the clinical covariate as fixed predictors with CNAs genomics windows as smoothing terms using the penalized additive Cox Proportional Hazards (PH) model. Most of the proposed prediction methods assume linearity of the CNAs genomic windows along with the clinical covariates. However, the continuous covariates can affect the hazard via more complicated nonlinear functional forms. Therefore, Cox PH model with continuous covariate are likely misspecified, because it is not fitting the correct functional form for the continuous covariates. Some reports of the work on combining the clinical covariates with high-dimensional genomic data in a clinical genomic prediction are based on standard Cox PH model. Most of them focus on applying variable selection to high-dimensional CNA genomic data. Our main interest is to propose a variable selection procedure to select important nonlinear effects from CNAs genomic-windows. Two different approaches of feature selection are presented which are discrete and shrinkage. Discrete feature selection is based on penalized univariate variable selection, which identify the subset of the CNAs genomic-windows have the strongest effects on the survival time, while feature selection by shrinkage works by adding a second penalty to the penalized partial log-likelihood, that leads to penalizing the smoothing coefficients in the model, as a result some of the smoothing coefficient are being set to the zero. For the NSCLC dataset, we find that the size of the tumor cells and spread cancer into the lymph nodes are significant factors that increase the hazard of the patients survival, and the estimate of the smooth log hazard ratio curves identify that some of the significant CNA genomic-windows contribute a higher or lower hazard of death to the survival of some significant CNA genomic-windows across the genome.
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Ansin, Elin. "An evaluation of the Cox-Snell residuals." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256665.
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It is common practice to use Cox-Snell residuals to check for overall goodness of tin survival models. We evaluate the presumed relation of unit exponentially dis-tributed residuals for a good model t and evaluate under some violations of themodel. This is done graphically with the usual graphs of Cox-Snell residual andformally using Kolmogorov-Smirnov goodness of t test. It is observed that residu-als from a correctly tted model follow unit exponential distribution. However, theCox-Snell residuals do not seem to be sensitive to the violations of the model.
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Pal, Subhamoy. "An Approach to Improving Test Powers in Cox Proportional Hazards Models." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1626893233789827.
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Andersson, Niklas. "Estimating Companies’ Survival in Financial Crisis : Using the Cox Proportional Hazards Model." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-225982.
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This master thesis is aimed towards answering the question What is the contribution from a company’s sector with regards to its survival of a financial crisis? with the sub question Can we use survival analysis on financial data to answer this?. Thus survival analysis is used to answer our main question which is seldom used on financial data. This is interesting since it will study how well survival analysis can be used on financial data at the same time as it will evaluate if all companies experiences a financial crisis in the same way. The dataset consists of all companies traded on the Swedish stock market during 2008. The results show that the survival method is very suitable the data that is used. The sector a company operated in has a significant effect. However the power is to low too give any indication of specific differences between the different sectors. Further on it is found that the group of smallest companies had much better survival than larger companies.
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Chi, Yunchan. "Comparisons of several goodness-of-fit tests for proportional hazards models /." The Ohio State University, 1988. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487591658175588.
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Sasieni, Peter D. "Beyond the Cox model : extensions of the model and alternative estimators /." Thesis, Connect to this title online; UW restricted, 1989. http://hdl.handle.net/1773/9556.
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Cheung, Tak-lun Alan, and 張德麟. "Modelling multivariate interval-censored and left-truncated survival data using proportional hazards model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29536637.
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Rudser, Kyle D. "Variable importance in predictive models : separating borrowing information and forming contrasts /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9609.
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Crouchley, Robert. "Testing for overdispersion in the parametric proportional hazards and some related models." Thesis, Imperial College London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.403689.
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Westberg, Ulf. "Applications of the PEXE-concept for maintenance policies and proportional hazards models." Licentiate thesis, Luleå tekniska universitet, 1994. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17974.
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Thompson, Kristina. "An Introduction to the Cox Proportional Hazards Model and Its Applications to Survival Analysis." Thesis, Southern Illinois University at Edwardsville, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1571931.
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Statistical modeling of lifetime data, or survival analysis, is studied in many fields, including medicine, information technology and economics. This type of data gives the time to a certain event, such as death in studies of cancer treatment, or time until a computer program crashes. Researchers are often interested in how covariates affect the time to event and wish to determine ways of incorporating such covariates into statistical models. Covariates are explanatory variables that are suspected to affect the lifetime of interest. Lifetime data are typically subject to censoring and this fact needs to be taken into account when choosing the statistical model.
D.R. Cox (1972) proposed a statistical model that can be used to explore the relationship between survival and various covariates and takes censoring into account. This is called the Cox proportional hazards (PH) model. In particular, the model will be presented and estimation procedures for parameters and functions of interest will be developed. Statistical properties of the resulting estimators will be derived and used in developing inference procedures.
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Söderberg, Daniel. "Model estimation of the longevity for cars registered in Sweden using survival analysis and Cox proportional hazards model." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-227520.
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Time-to-event data is used in this thesis to analyze private cars’ longevity in Sweden. Thedataset is provided by Trafikanalys and contains all registered, deregistered or temporary deregisteredcars in Sweden during the time period 2000 - 2012.A Cox proportional hazards model is fitted, including variables such as car manufacturer andcar body. The results show that directly imported cars have a much shorter median survivalcompared to non-imported cars. The convertible cars have the longest median survival amongthe five different car bodies. Sedan and station wagon body types have the shortest mediansurvival. Volvo and Mercedes have the longest survival while Renault, Ford and Opel have theshortest survival. The model fits the data reasonably well, and the assumption of proportionalhazards holds for most of the variables.
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Gwaze, Arnold Rumosa. "A cox proportional hazard model for mid-point imputed interval censored data." Thesis, University of Fort Hare, 2011. http://hdl.handle.net/10353/385.
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There has been an increasing interest in survival analysis with interval-censored data, where the event of interest (such as infection with a disease) is not observed exactly but only known to happen between two examination times. However, because so much research has been focused on right-censored data, so many statistical tests and techniques are available for right-censoring methods, hence interval-censoring methods are not as abundant as those for right-censored data. In this study, right-censoring methods are used to fit a proportional hazards model to some interval-censored data. Transformation of the interval-censored observations was done using a method called mid-point imputation, a method which assumes that an event occurs at some midpoint of its recorded interval. Results obtained gave conservative regression estimates but a comparison with the conventional methods showed that the estimates were not significantly different. However, the censoring mechanism and interval lengths should be given serious consideration before deciding on using mid-point imputation on interval-censored data.
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Fei, Mingwei. "A study of the robustness of Cox's proportional hazards model used in testing for covariate effects." Kansas State University, 2012. http://hdl.handle.net/2097/13528.
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Master of ArtsDepartment of Statistics
Paul Nelson
There are two important statistical models for multivariate survival analysis, proportional hazards(PH) models and accelerated failure time(AFT) model. PH analysis is most commonly used multivariate approach for analysing survival time data. For example, in clinical investigations where several (known) quantities or covariates, potentially affect patient prognosis, it is often desirable to investigate one factor effect adjust for the impact of others. This report offered a solution to choose appropriate model in testing covariate effects under different situations. In real life, we are very likely to just have limited sample size and censoring rates(people dropping off), which cause difficulty in statistical analysis. In this report, each dataset is randomly repeated 1000 times from three different distributions (Weibull, Lognormal and Loglogistc) with combination of sample sizes and censoring rates. Then both models are evaluated by hypothesis testing of covariate effect using the simulated data using the derived statistics, power, type I error rate and covergence rate for each situation. We would recommend PH method when sample size is small(n<20) and censoring rate is high(p>0.8). In this case, both PH and AFT analyses may not be suitable for hypothesis testing, but PH analysis is more robust and consistent than AFT analysis. And when sample size is 20 or above and censoring rate is 0.8 or below, AFT analysis will have slight higher convergence rate and power than PH, but not much improvement in Type I error rates when sample size is big(n>50) and censoring rate is low(p<0.3). Considering the privilege of not requiring knowledge of distribution for PH analysis, we concluded that PH analysis is robust in hypothesis testing for covariate effects using data generated from an AFT model.
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Wang, Wen-Chyi. "Regularized variable selection in proportional hazards model using area under receiver operating characteristic curve criterion." College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9972.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2009.Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Imbayarwo-Chikosi, Venancio Edward. "Genetic evaluation of functional longevity in South African Holstein cattle using a proportional hazards model." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/98128.
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Thesis (PhD)--Stellenbosch University, 2015.ENGLISH ABSTRACT: Improvement and selection of superior animals for longevity is a viable complimentary strategy for increasing functional longevity of Holstein dairy cattle. Genetic evaluation of animals for functional longevity is a prerequisite for improvement of the trait. This study was carried out to determine non-genetic factors that influence functional longevity in South African Holstein cattle, as well as to estimate genetic parameters for functional longevity, estimate sire breeding values, and to establish the contribution of conformation traits to the risk of cows being culled from dairy herds. Analyses were carried out using records of 166 222 daughters of 2 051 sires from 1 545 herds in six regions of South Africa. The regions were the Western Cape, Eastern Cape, Northern Cape, Free State, Kwa-Zulu Natal and the combined Gauteng & North Western Provinces. Data were analysed using a piecewise Weibull sire-maternal grandsire model in which the baseline hazard was assumed to change at 270 and 380 days in milk as well as at drying-off. The fixed effects model comprised of the time-independent effect of age at first calving, the combined time-dependent effects of region x year of calving, number of lactations x within-herd milk production class, year of calving x within-herd protein and year of calving x fat percentage production class. Model also had the combined time-dependent effect of herd size x annual herd size change. The random herd-year and sire effects were later included in the model for the estimation of sire and herd-year variance components. Analyses indicated a decline in the survival function, an indicator of functional longevity, among cows that calved for the first time in 2000, 2004 and 2008. Cows that delivered their first calf in 2000 had better survival functions that those that calved for the first time in 2004 and 2008. All fitted effects significantly contributed to the risk of a cow being culled from a herd. Within-herd milk production made the largest contribution to the risk of a cow being culled from a herd. Survival was best in the Northern Cape and worst in Eastern Cape. The risk of being culled was the highest for cows in the second stage of lactation (271- 380 days), when the entire lactation period was considered. High producing cows were more likely not to be culled from a herd than poor producing cows. Cows were more likely to be culled for low protein production percentage than within-herd fat production percentage. The risk of being culled was low for heifers calving at 20 to 25 months of age. The higher risk of culling among cows with multiple lactations indicated the culling policy of dairy farmers to retain a higher number of younger cows than older cows in herds. A decrease in herd size was indicative of a high culling rate. Effective heritability was 0.109. Breeding values ranged from 2.12 for the best cows to -4.80 for the worst cows. This implied that the best and the worst cows were 2.12 times and 4.80 more likely to be culled from herds than the average cow respectively. Genetic trends for functional longevity indicated a marginal decline in sire estimated breeding values. This corresponded with the phenotypic decline in the survivor function observed in cows that calved in 200, 2004 and 2008. All udder, rump, body, feet and leg type traits, with the exception of rear leg side, significantly influenced functional longevity. Farmers culled cows mainly of extremely poor type with a tendency to retain animals with poor to very good structure. Udder traits contributed the most to the risk of a cow being culled. Cows were more likely to be culled for poor scores received for rear teat placement, rear udder height and fore udder attachment, than for any of the other conformation type traits. The contribution of angularity to the risk of being culled was also high, indicating a preference for highly angular bodied cows. Conformation type traits can therefore be used as indicator traits for functional longevity in Holstein cattle, especially when selection and culling is done based on very poor scores for udder traits. The study indicated that genetic improvement in functional longevity can be achieved with the evaluation and selection of dairy sires. There is therefore a wide scope for including functional longevity in the selection objectives for South African Holstein cattle. There is a need to develop appropriate models to ensure that the national dairy industry can benefit from using a Weibull piecewise model.
AFRIKAANSE OPSOMMING: Die verbetering en seleksie van voortreflike diere vir langlewendheid is 'n lewensvatbare en aanvullende strategie vir die verhoging van die funksionele langlewendheid van Holstein melkbeeste. Die genetiese evaluasie van melkproduserende diere ten opsigte van hierdie eienskap is ʼn voorvereiste om verbetering in die eienskap moontlik te maak. Die studie is uitgevoer om nie-genetiese faktore wat die funksionele langlewendheid van Suid-Afrikaanse Holsteins beïnvloed te bepaal, om genetiese parameters vir langlewendheid en vaar teeltwaardes vir dié eienskap te bepaal en ook om die bydrae van bouvorm eienskappe tot die waarskynlikheid van koeie om uitgeskot te word, te bepaal. ʼn Databasis bestaande uit rekords van 166 222 dogters van 2 051 vaars, wat in ses streke in Suid-Afrika (Wes-Kaap, Oos-Kaap, Noord-Kaap, Vrystaat, KwaZulu-Natal en die gekombineerde Gauteng en Noord-Wes Provinsies) voorgekom het, is vir die studie gebruik. Data is ontleed met behulp van 'n stuksgewyse Weibull vader-moeder grootvader model, waarin die basislyn risiko aanvaar is om te verander op 270, 380 dae na kalwing en by afdroog van die melkkoeie. Die vaste effek model het die tyd-onafhanklike effek van ouderdom met eerste kalwing en die tyd-afhanklike effekte van streek en jaar van kalwing, aantal laktasies en binne-kudde melkproduksie klas, binne-kudde proteïen en vet persent produksie klasse volgens jaar van kalwing, asook die gekombineerde tyd-afhanklike effek van kuddegrootte en jaarlikse verandering in kudde grootte, ingesluit. Die ewekansige trop-jaar en vaar effekte is later opgeneem in die model vir bepaling van die vaar- en kudde-jaar variansie komponente. Ontledings het aangedui dat daar 'n algemene afname in die oorlewingsfunksie, wat ʼn aanduiding van funksionele langlewendheid is, was in koeie wat vir die eerste keer in 2000, 2004 en 2008 gekalf het. Koeie wat vir die eerste keer in 2000 gekalf het, het ʼn hoër waarde vir die oorlewingsfunksie gehad as koeie wat onderskeidelik in 2004 en 2008 gekalf het. Alle vaste effekte het betekenisvol tot die waarskynlikheid van ʼn koei om uitgeskot te word, bygedra. Binne-kudde melkproduksie het die grootste bydrae tot die waarskynlikheid van ʼn koei om uitgeskot te word, gemaak. Wanneer die totale laktasieperiode in ag geneem is, was daar gevind dat koeie wat in die tweede fase van laktasie (271-380 dae) die hoogste risiko ervaar het om uitgeskot te word. Hoë produseerders, wanneer hulle met lae produseerders vergelyk is, was minder geneig om uitgeskot te word. Koeie was meer geneig om uitgeskot te word vir lae proteïen produksie persentasie as binne-kudde vet produksie persentasie. Die waarskynlikheid om uitgeskot te word, was laag vir verse wat op ʼn ouderdom van 20 tot 25 maande gekalf het. Die hoër risiko van uitskot van koeie met veelvuldige laktasies het aangedui dat melkboere geneig was om meer jonger koeie te behou en ouer koeie uit te skot. ʼn Afname in kuddegrootte was aanduidend van ʼn hoë uitskotpersentasie. Die effektiewe oorerflikheid was 0.109, met teelwaardes wat van 2.12 vir die hoogste produserende koeie tot -4.80 vir die laagste produserende koeie, gewissel het. Hierdie waardes het dus aangedui dat hoë produseerders en swak produseerders onderskeidelik ʼn 2.12 en 4.80 groter kans gehad het om uitgeskot te kan word. Genetiese tendense vir funksionele langlewendheid het aangedui dat daar 'n marginale afname in vaar beraamde teelwaardes vir funksionele langlewendheid was. Dit het ooreengestem met die waargenome fenotipiese afname in die oorlewingsfunksie wat in 2000, 2004 en 2008 gekalf het. Alle bou-tipe eienskappe (uier, agterkwart, liggaam, voet en been), met die uitsondering van die agterbeen kant, het funksionele langlewendheid betekenisvol beïnvloed. Boere het koeie hoofsaaklik op uiters swak boutipe uitgeskot, met 'n neiging om diere wat as ʼn swak tot uiters goeie bouvorm geklassifiseer is, te behou. Uier eienskappe het die meeste tot die waarskynlikheid van ʼn koei om uitgeskot te word, bygedra. Koeie met ʼn lae gradering vir agterste speen plasing, agter-uier hoogte en voor-uier aanhegting se waarskynlikheid om uitgeskot te word, was groter. Die bydrae van hoekigheid tot die waarskynlikheid van ʼn koei om uitgeskot te word was hoog, wat ʼn aanduiding van ʼn voorkeur vir 'n baie growwe hoekigheid was. Bouvorm tipe eienskappe kan dus gebruik word as indikator eienskappe vir funksionele langlewendheid in Holstein melkbeeste, veral wanneer in ag geneem word dat die meeste melkboere seleksie op grond van swak gradering van uier eienskappe doen. Die studie het aangedui dat genetiese verbetering in funksionele langlewendheid teweeg gebring kan word met die evaluering en seleksie van Holstein vaars. Daar is dus ʼn geleentheid vir die insluiting van funksionele langlewendheid in die seleksie doelwitte vir Suid-Afrikaanse Holstein beeste. Daar is ʼn behoefte om toepaslike modelle te ontwikkel wat met die toepassing daarvan, sal verseker dat die plaaslike melkbedryf sal kan voordeel trek deur die gebruik van 'n stuksgewyse Weibull model benadering.
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Mlotshwa, Vintia Philile. "Modelling hepatotoxicity in HIV/TB co-infected patients: extensions of the Cox Proportional Hazards Model." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32806.
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Hepatotoxicity which is also known as liver damage is mainly caused by intake of medicine. It is common among patients who are co-administering Tuberculosis (TB) treatment and the antiretroviral therapy (ART) for the Human Immunodeficiency Viruses (HIV). If severe, hepatotoxicity sometimes necessitates cessation or interruption of treatment. Therefore, understanding, monitoring and managing hepatotoxicity in patients co-infected with TB and HIV is crucial for optimal treatment outcomes. Hepatotoxicity has been investigated in patients coinfected with TB and HIV, however, most studies have analyzed only the first occurrence of hepatotoxicity and discarded information relating to the resolution and recurrence of hepatotoxicity. Data from the ‘Starting Antiretroviral therapy at three Points in Tuberculosis' (SAPiT) trial is used in this project. This was a trial that was instrumental in finalizing treatment guidelines for patients co-infected with HIV and TB in South Africa. The clinical objectives of this project are to estimate incidence rates and determine risk factors associated with hepatotoxicity. The statistical objectives are to fit a Cox regression model, the resolution model of hepatotoxicity, and the extended Cox models for recurring events, including the Andersen Gill (AG) model, the Shared frailty model, the Prentice, Williams and Peterson (PWP) total time (TT) model, the PWP gap time (GT) model, as well as a Cox based recurrent model, that models only the second occurrence of hepatotoxicity. There were 593 patients assessed for hepatotoxicity in the study, 30% (179/593) developed the first occurrence of hepatotoxicity (grade >=1) and 2% (13/593) developed severe hepatotoxicity (grade >=3). Resolved cases (grade = 0) are 76% (136/179) and recurring cases (grade >=1) 24% (32/136). In the Cox multivariable analyses: time-varying treatment arm, older patients, alcohol consumption, low baseline total bilirubin and a positive baseline Hepatitis B surface antigen status, were associated with a higher risk of developing the first occurrence of hepatotoxicity. The extended Cox models (AG model, Shared frailty model, PWP TT model and PWP GT model) in combination identified that: time-varying treatment arm, older patients, alcohol consumption, baseline CD4 count that is greater than 50 cells per mm3 , low baseline total bilirubin, and a positive baseline Hepatitis B surface antigen status were associated with an increased risk of developing recurrent hepatotoxicity. In the resolution model multivariable analyses; non-consumers of alcohol and an abnormal liver function tests at baseline, were associated with an increased chance of resolving the first occurrence of hepatotoxicity. In the multivariable analyses of the recurrent model: younger patients and the time-varying treatment arm were associated with the development of the second occurrence hepatotoxicity. Since the Cox regression model utilized data up to the first occurrence of hepatotoxicity, in some instances, the time-varying treatment effect based on the Cox regression model was closer to unity and marginally significant. And the corresponding effect based on the recurrent event models (AG model, Shared frailty model, PWP TT model, PWP GT model and the recurrent model), that utilized data of the first and second occurrence of hepatotoxicity, generally produced a time-varying treatment effect slightly far from unity with a strong statistical significance. This trend was similar for other predictors of hepatotoxicity, like CD4 count and alcohol consumption. In conclusion, hepatotoxicity is common in this study, however, it is often transient or mild and did not necessitate treatment interruption. However, close monitoring of patients especially in the first 5 months of TB-treatment is recommended. The PWP TT model seemed to be the best model for modelling recurring hepatotoxicity, since the identified risk factors that were associated with hepatotoxicity, changed from the first occurrence of hepatotoxicity to the second occurrence of hepatotoxicity.
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Latorre, Maria do Rosario Dias de Oliveira. "Comparação entre alguns métodos estatísticos em análise de sobrevivência: aplicação em uma coorte de pacientes com câncer de pênis." Universidade de São Paulo, 1996. http://www.teses.usp.br/teses/disponiveis/6/6132/tde-12112014-153823/.
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O objetivo deste trabalho foi comparar o desempenho do modelo de riscos proporcionais de Cox convencional, modelo de Cox modificado quando os riscos não são proporcionais e o modelo de análise de sobrevida baseado na teoria de processos de contagem. Para tanto utilizou-se uma coorte de 648 pacientes portadores de câncer de pênis, atendidos no Departamento de Cirurgia Pélvica do Hospital A. C. Camargo, no período de 1953 a 1985. Dessa coorte foram selecionadas três amostras com o objetivo de validar internamente os resultados da análise de sobrevida do banco de dados original. Os resultados do modelo de riscos proporcionais de Cox, no banco de dados original, foram confirmados por uma das amostras desse conjunto de dados. Apenas o estadiamento N foi confirmado como fator prognóstico também nas outras duas amostras. O modelo de riscos proporcionais de Cox e o modelo de análise de sobrevida baseado na teoria de processos de contagem apresentaram resultados semelhantes, na definição dos fatores prognósticos dessa coorte de pacientes com câncer de pênis. O modelo utilizando processos de contagem é mais sofisticado, do ponto de vista matemático. Porém o modelo de Cox está disponível em grande número de pacotes estatísticos e a interpretação de seus coeficientes se faz com maior facilidade. Por isso, talvez, continue a ser a técnica estatística mais utilizada quando o objetivo do estudo é definir fatores prognósticos e grupos de risco. Os fatores prognósticos para a sobrevida de pacientes com câncer de pênis foram os estadiamentos T e N e o grau de diferenciação do tumor. Esses resultados foram ajustados pelo ano de início de tratamento no Hospital A.C. Camargo. Os pacientes com prognóstico favorável foram os que apresentaram tumor pequeno, sem presença de linfonodos clinicamente positivos, e tumor bem diferenciado.The aim of this study was to compare the performance of the Cox proportional hazards model, the Cox model with time-dependent covariates and the survival model using the counting process theory. These methods were applied in a cohort of 648 patients with penile cancer treated at the Department of Pelvic Surgery, Hospital A.C. Camargo (São Paulo-Brazil), between 1953 and 1985. Three samples were selected from the total database in order to check the internal validity. The prognostic factors selected using the Cox proportional hazards model were the same in one sample. The only prognostic factor selected in all samples was the N stage. The T and N stages, and the grade of differentiation were independent prognostic factors of survival using both the Cox proportional hazards model and the survival,model using the counting process theory. The statistical significance was the same and even the values of estimation of the coefficients were very close. The survival model using the counting process is more sophisticated from the mathematical point of view, but the Cox model is more available in statistical software, and, probably because of this, is more applied in survival analysis than the model using the counting processo Patients with small tumors, clinically negatives nodes and well differentiated tumors showed a favorable prognosis. These results were adjusted by year of the beginning in the study.
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Qi, Lihong. "Analysis of failure time data under risk set sampling and missing covariates /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/9550.
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Zhao, Yonggang. "The general linear model for censored data." The Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=osu1054781042.
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Frejd, Ellen, and Jenny Sjödin. "Är kognitiva test relaterade till demens? : En utvidgning av Cox Proportional Hazards Model med tidsvarierande kovariat." Thesis, Umeå universitet, Statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184984.
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Syftet med uppsatsen är att undersöka samband mellan kognitiva test och insjuknande i demens. Demens är en samlingsdiagnos för sjukdomar som är kopplade till nedsatt kognitiv förmåga. Symptom inkluderar försämrad minnesfunktion och personlighetsförändringar. Data är insamlad av den longitudinella studien Betula vid Umeå universitet. Vid upprepade testtillfällen genomfördes minnestester för att mäta deltagarnas minnesfunktion och kognitiva förmåga. Minnestestresultat analyseras med en utvidgning av Cox Proportional Hazards Model med tidsvarierande kovariat. Genom att analysera testresultat som varierar över tid erhölls uppdaterad information om deltagarnas kognitiva tillstånd. Vidare jämförs den utvidgade modellen med en klassisk Coxmodell med baslinjedata. Modellering inkluderar även kontrollvariabeln ApoE4 som är en genvariant som innebär förhöjd demensrisk. Resultaten visar samband mellan demens och test som mäter episodminne och visuospatial förmåga. Den utvidgade modellen med tidsvarierande testresultat föredras som modellval.
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Bertke, Stephen J. "A Simulation Study of the Cox Proportional Hazards Model and the Nested Case-Control Study Design." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321495.
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Dias, Daniel Alexandre Baptista. "Testes de especificação ao modelo de Hazard proporcional." Master's thesis, Instituto Superior de Economia e Gestão, 2005. http://hdl.handle.net/10400.5/13257.
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Mestrado em Econometria Aplicada e PrevisãoO estudo dos factores que influenciam o tempo entre dois acontecimentos é um tópico comum a vários ramos das ciências económicas. Na literatura, este tipo de análise é conhecida por "Análise da Duração n e os modelos estatísticos/econométricos que lhe estão associados são os "Modelos de Duroção". No trabalho que se segue iremos apresentar mn conjunto de testes de especificação ao modelo de Hazard Pro porcional assim como iremos analisar as propriedades de amostras finitas de testes de especificação gerais, em particular, testes de momentos condicionais.
The analysis of factors inflnencing the time between evento; is a topic common to severa!areas of the economic sciences. ln the literature, this type of ana1ysis is known as Duration Analysis and the inherent econometric models are the Duration Models. ln this work, we will present some specification tests of the Proportional Hazard model and we will also analyse the finite sample properties of general specification tests, in particular, conditional moments tests
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Boudreau, Christian. "Duration Data Analysis in Longitudinal Survey." Thesis, University of Waterloo, 2003. http://hdl.handle.net/10012/1043.
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Considerable amounts of event history data are collected through longitudinal surveys. These surveys have many particularities or features that are the results of the dynamic nature of the population under study and of the fact that data collected through longitudinal surveys involve the use of complex survey designs, with clustering and stratification. These particularities include: attrition, seam-effect, censoring, left-truncation and complications in the variance estimation due to the use of complex survey designs. This thesis focuses on the last two points.
Statistical methods based on the stratified Cox proportional hazards model that account for intra-cluster dependence, when the sampling design is uninformative, are proposed. This is achieved using the theory of estimating equations in conjunction with empirical process theory. Issues concerning analytic inference from survey data and the use of weighted versus unweighted procedures are also discussed. The proposed methodology is applied to data from the U. S. Survey of Income and Program Participation (SIPP) and data from the Canadian Survey of Labour and Income Dynamics (SLID).
Finally, different statistical methods for handling left-truncated sojourns are explored and compared. These include the conditional partial likelihood and other methods, based on the Exponential or the Weibull distributions.
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Sandström, Caroline, and Karl Norling. "Female longevity : A survival analysis on 19th century women using the Cox Proportional Hazard model." Thesis, Umeå universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-49700.
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Chen, Lu. "Semi-parametric analysis of failure time data from case-control family studies on candidate genes /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/9573.
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Cai, Jianwen. "Generalized estimating equations for censored multivariate failure time data /." Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/9581.
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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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Demissie, Mathewos S. "Investigating center effects in a multi-center clinical trial study using a parametric proportional hazards meta-analysis model." College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9588.
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Thesis (M.S.) -- University of Maryland, College Park, 2009.Thesis research directed by: Mathematical Statistics Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Charoensawat, Supada. "A likelihood approach based upon the proportional hazards model for SROC modelling in meta-analysis of diagnostic studies." Thesis, University of Reading, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627926.
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The number of meta-analysis of diagnostic studies is increasing and the models which deal with the summary receiver operating characteristic (SROC) have become quite popular. Many of these models have reached considerable statistical complexity, required expertise and knowledge. Here" a model named the proportional hazard model (PHM) is developed. The PHM model has a simple form and is easy to interpret. There is only one parameter of interest 0, which is called the diagnostic accuracy and has the interpretation that the smaller 0 is, the higher the diagnostic accuracy.
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Ekman, Anna. "Variable selection for the Cox proportional hazards model : A simulation study comparing the stepwise, lasso and bootstrap approach." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-130521.
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In a regression setting with a number of measured covariates not all may be relevant to the response. By reducing the numbers of covariates included in the final model we could improve its prediction accurarcy as well as making it easier to interpret. In survival analysis, the study of time-to-event data, the most common form of regression is the semi-parametric Cox proportional hazard (PH) model. In this thesis we have compared three different ways to perform variable selection in the Cox PH model, stepwise regression, lasso and bootstrap. By simulating survival data we could control which covariates that were significant for the response. Fitting the Cox PH model to these data using the three different variable selection methods we could evaluate how well each method performs in finding the correct model. We found that while bootstrap in some cases could improve the stepwise approach its performance is strongly effected by the choice of inclusion frequency. Lasso performed equivalent or slightly better than the stepwise method for data with weak effects. However, when the data instead consists of strong effects, the performance of stepwise is considerably better than the performance of lasso.Vid regression söks sambandet mellan en beroende variabel och en eller flera förklarande variabler. Även om vi har tillgång till många förklarande variabler är det dock inte säkert att alla påverkar den beroende variabeln. Genom att minska antalet variabler som inkluderas i den slutgiltiga modellen kan man förbättra dess prediktionsförmåga samtidigt som den blir lättare att tolka. Inom överlevnadslys är en av de vanligaste regressionsmetoderna den semi-parametriska Cox proportional hazard (PH) model. I den här uppsatsen har vi jämfört tre olika metoder för variabel selektion i Cox PH model, stegvis regression, lasso och bootstrap. Genom att simulera överlevnadsdata kan vi styra vilka variabler som påverkar den beroende variabelen. Det blir då möjligt att utvärdera hur väl de olika metoderna lyckas med att inkludera dessa variabler i den slutgiltiga Cox PH model. Vi fann att bootstrap i vissa situationer gav bättre resultat än den stegvisa regressionen, dock varierar resultatet väldigt mycket beroende på valet av inklusionsfrekvens. Resultaten av lasso och stegvis regression är likvärdiga, eller till fördel för lasso, så länge datat innehåller svagare effekter. När datat istället består av starkare effekter ger dock den stegvisa regressionen mycket bättre resultat än lasso.
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Pelagia, Ioanna. "Variable selection of fixed effects and frailties for Cox Proportional Hazard frailty models and competing risks frailty models." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/variable-selection-of-fixed-effects-and-frailties-for-cox-proportional-hazard-frailty-models-and-competing-risks-frailty-models(c75c6314-f43e-4d69-a2de-942bece6a404).html.
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This thesis focuses on two fundamental topics, specifically in medical statistics: the modelling of correlated survival datasets and the variable selection of the significant covariates and random effects. In particular, two types of survival data are considered: the classical survival datasets, where subjects are likely to experience only one type of event and the competing risks datasets, where subjects are likely to experience one of several types of event. In Chapter 2, among other topics, we highlight the importance of adding frailty terms on the proposed models in order to account for the association between the survival time and characteristics of subjects/groups. The main novelty of this thesis is to simultaneously select fixed effects and frailty terms through the proposed statistical models for each survival dataset. Chapter 3 covers the analysis of the classical survival dataset through the proposed Cox Proportional Hazard (PH) model. Utilizing a Cox PH frailty model, may increase the dimension of variable components and estimation of the unknown coefficients becomes very challenging. The method proposed for the analysis of classical survival datasets involves simultaneous variable selection on both fixed effects and frailty terms through penalty functions. The benefit of penalty functions is that they identify the non-significant parameters and set them to have a zero effect in the model. Hence, the idea is to 'doubly-penalize' the partial likelihood of the Cox PH frailty model; one penalty for each term. Estimation and selection implemented through Newton-Raphson algorithms, whereas closed iterative forms for the estimation and selection of fixed effects and prediction of frailty terms were obtained. For the selection of frailty terms, penalties imposed on their variances since frailties are random effects. Based on the same idea, we further extend the simultaneous variable selection in the competing risks datasets in Chapter 4, using extended cause-specific frailty models. Two different scenarios are considered for frailty terms; in the first case we consider that frailty terms vary among different types of events (similar to the fixed effects) whereas in the second case we consider shared frailties over all the types of events. Moreover, our 'individual penalization' approach allows for one covariate to be significant for some types of events, in contrast to the frequently used 'group-penalization' where a covariate is entirely removed when it is not significant over all the events. For both proposed methods, simulation studies were conduced and showed that the proposed procedure followed for each analysis works well in simultaneously selecting and estimating significant fixed effects and frailty terms. The proposed methods are also applied to real datasets analysis; Kidney catheter infections, Diabetes Type 2 and Breast Cancer datasets. Association of the survival times and unmeasured characteristics of the subjects was studied as well as a variable selection for fixed effects and frailties implemented successfully.
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Minya, Kristoffer. "Överlevnadsanalys i tjänsteverksamhet : Tidspåverkan i överklagandeprocessen på Migrationsverket." Thesis, Linköpings universitet, Statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110428.
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Migrationsverket är en myndighet som prövar ansökningar från personer som vill söka skydd, ha medborgarskap, studera eller vill jobba i Sverige. Då det på senare tid varit en stor ökning i dessa ansökningar har tiden för vilket ett beslut tar ökat. Varje typ av ansökning (exempelvis medborgarskap) är en process som består av flera steg. Hur beslutet går igenom dessa steg kallas för flöde. Migrationsverket vill därför öka sin flödeseffektivitet. När beslutet är klart och personen tagit del av det men inte är nöjd kan denne överklaga. Detta är en av de mest komplexa processerna på Migrationsverket. Syftet är analysera hur lång tid denna process tar och vilka steg i processen som påverkar tiden. Ett steg (som senare visar sig ha en stor effekt på tiden) är yttranden. Det är när domstolen begär information om vad personen som överklagar har att säga om varför denne överklagar. För att analysera detta var två metoder relevanta, accelerated failure time (AFT) och \multi-state models (MSM). Den ena kan predicera tid till händelse (AFT) medan den andra kan analysera effekten av tidspåverkan (MSM) i stegen. Yttranden tidigt i processen har stor betydelse för hur snabbt en överklagan får en dom samtidigt som att antal yttranden ökar tiden enormt. Det finns andra faktorer som påverkar tiden men inte i så stor grad som yttranden. Då yttranden tidigt i processen samtidigt som antal yttranden har betydelse kan flödeseffektiviteten ökas med att ta tid på sig att skriva ett informativt yttrande som gör att domstolen inte behöver begära flera yttranden.The Swedish Migration Board is an agency that review applications from individuals who wish to seek shelter, have citizenship, study or want to work in Sweden. In recent time there has been a large increase in applications and the time for which a decision is made has increased. Each type of application (such as citizenship) is a process consisting of several stages. How the decision is going through these steps is called flow. The Swedish Migration Board would therefore like to increase their flow efficiency. When the decision is made and the person has take part of it but is not satisfied, he can appeal. This is one of the most complex processes at the Board. The aim is to analyze how long this process will take and what steps in the process affects the time. One step (which was later found to have a significant effect on time) is opinions. This is when the court requests information on what the person is appealing has to say about why he is appealing. To analyze this, two methods were relevant, accelerated failure time (AFT) and the multi-state models (MSM). One can predict time to event (AFT), the other to analyze the effect of time-manipulation (MSM) in the flow. Opinions early in the process is crucial to how quickly an appeal get judgment while the number of opinions increases the time enormously. There are other factors that affect the time but not so much as opinions. The flow efficiency can be increased by taking time to write an informative opinion which allows the court need not to ask for more opinions.
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Drepper, Bettina [Verfasser], and Gerard van den [Akademischer Betreuer] Berg. "The Multivariate Mixed Proportional Hazard Model: Applications and Extensions / Bettina Drepper. Betreuer: Gerard van den Berg." Mannheim : Universitätsbibliothek Mannheim, 2013. http://d-nb.info/1046273035/34.
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Kao, Xin-ru, and 高欣如. "Discussion on Cox Proportional Hazards Assumption and Application of Extended Hazard Model." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/wh9svf.
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碩士國立中央大學
統計研究所
97
The Cox proportional hazards model has been widely used to describe the relationship between survival information and covariates. The validity to apply the Cox model for data is usually based on checking the proportional hazards assumption. It’s an interesting problem to investigate whether checking this assumption is sufficient as an evidence to fit data with the Cox model. On the other hand, when proportional hazards assumption fails, the Accelerated Failure Time (AFT) model is a popular alternative to the Cox model. However, when data include time-dependent covariates there are no convenient tools to check if AFT is appropriate for the data. An general class model termed “extended hazard model”, which contains the Cox and AFT models as its special case may be helpful to study the above problems. Because under the nested structure, we may test the fit of Cox and AFT models for data. Finally, we demonstrate the new model through a case study of Taiwanese HIV/AIDS cohort data.
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Wu, Bai Rong. "Condition based maintenance using proportional hazards model." Thesis, 2009. http://spectrum.library.concordia.ca/976442/1/MR63089.pdf.
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Condition-based maintenance (CBM) is an advanced maintenance strategy in which maintenance actions are scheduled based on both the age data and condition monitoring information. Proportional Hazards Model (PHM) is a powerful statistical tool for estimating the equipment failure rate under condition monitoring. Effective CBM using PHM can decrease the overall maintenance costs by reducing unnecessary scheduled preventive maintenance actions. In CBM using PHM, main optimization objectives including minimizing maintenance costs and maximizing equipment reliability typically conflict to each other. But the reported research only focuses on single-objective. In this thesis, we propose a multiple-objective CBM optimization approach based on physical programming, which can systematically balance the tradeoff between the optimization objectives and find the optimal solution that best represents the decision maker's preference on the objectives. In CBM using PHM, the accuracy of parameter estimation greatly affects the accuracy of the model in representing and predicting the equipment health condition. Traditional optimization methods such as Newton's methods are inaccurate because they can only find local optimal value in parameter estimation. In this thesis, we develop an approach based on Genetic Algorithms (GA) for PHM parameter estimation and this approach can improve the accuracy of parameter estimation significantly. To illustrate the proposed approaches, we conduct two case studies using real-world vibration monitoring data, shearing pump bearings in a food processing plant and Gould pump bearings at Canadian Kraft Mill. The proposed approaches contribute to the general knowledge of condition based maintenance, and have the potential to greatly benefit various industries.
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Sun, Kang Hua, and 孫康華. "Poor Family in Chia-Yi : Proportional Hazards Model." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/09935935360322373562.
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LAI, YU-HSUAN, and 賴毓宣. "Proportional Hazard Model and Proportional Odds Model under Dependent Current Status Data." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/z843bh.
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碩士國立中正大學
數學系統計科學研究所
107
This thesis focuses on the estimation of the parameter under the regression model on current status data with dependent censoring, using proportional hazard model and proportional odds model to analyze the data. We consider the failure time to be related to the observation time, so we use Archimedean Copula to specify the dependency. According to Hsieh and Chen (2014), we can estimate the survival function of the failure time. We consider a general regression model and construct two proposed methods to obtain the parameter. Then, examine the finite sample performance of the proposed estimation procedures via simulation studies. Finally, we apply our proposed methodologies to analyze the tumorigenicity data on mice.