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1
Gandy, Axel. "Directed model checks for regression models from survival analysis." Berlin Logos-Ver, 2005. http://deposit.ddb.de/cgi-bin/dokserv?id=2766731&prov=M&dok_var=1&dok_ext=htm.
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Gandy, Axel. "Directed model checks for regression models from survival analysis /." Berlin : Logos-Ver, 2006. http://deposit.ddb.de/cgi-bin/dokserv?id=2766731&prov=M&dok_var=1&dok_ext=htm.
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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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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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Yuan, Xingchen. "Survival Model and Estimation for Lung Cancer Patients." Digital Commons @ East Tennessee State University, 2005. https://dc.etsu.edu/etd/1002.
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Lung cancer is the most frequent fatal cancer in the United States. Following the notion in actuarial math analysis, we assume an exponential form for the baseline hazard function and combine Cox proportional hazard regression for the survival study of a group of lung cancer patients. The covariates in the hazard function are estimated by maximum likelihood estimation following the proportional hazards regression analysis. Although the proportional hazards model does not give an explicit baseline hazard function, the baseline hazard function can be estimated by fitting the data with a non-linear least square technique. The survival model is then examined by a neural network simulation. The neural network learns the survival pattern from available hospital data and gives survival prediction for random covariate combinations. The simulation results support the covariate estimation in the survival model.
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Wang, Hongwei. "Effect of Risk and Prognosis Factors on Breast Cancer Survival: Study of a Large Dataset with a Long Term Follow-up." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_theses/116.
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The main goal of this study is to seek the effects of some risk and prognostic factors contributing to survival of female invasive breast cancer in United States. The study presents the survival analysis for the adult female invasive breast cancer based on the datasets chosen from the Surveillance Epidemiology and End Results (SEER) program of National Cancer Institute (NCI). In this study, the Cox proportional hazard regression model and logistic regression model were employed for statistical analysis. The odds ratios (OR), hazard ratios (HR) and confidence interval (C.I.) were obtained for the risk and prognosis factors. The study results showed that some risk and prognosis factors, such as the demographic factors (race and age), social and family factor (marital status), biomedical factors (tumor size, disease stage, tumor markers and tumor cell differentiation level etc.) and type of treatment patients received had significant effects on survival of the female invasive breast cancer patients.
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Dawson, Amanda Caroline St Vincent???s Hospital Clinical School UNSW. "Evaluation of novel molecular markers from the WNT pathway : a stepwise regression model for pancreatic cancer survival." Awarded by:University of New South Wales. St Vincent???s Hospital Clinical School, 2007. http://handle.unsw.edu.au/1959.4/31528.
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Optimisation of the conventional tripartite of pancreatic cancer (PC) treatment have led to significant improvements in mortality, however further knowledge of the underlying molecular processes is still required. Transcript profiling of mRNA expression of over 44K genes with microarray technology demonstrated upregulation of secreted frizzled related protein 4 (sFRP4) and ??-catenin in PC compared to normal pancreata. Their pathway ??? Wnt signalling is integral to transcriptional regulation and aberrations in these molecules are critical in the development of many human malignancies. Immunohistochemistry protocols were evaluated by two independent blinded examiners for antigen expression differences associated with survival patterns in 140 patients with biopsy verified PC and a subset of 23 normal pancreata with substantial observer agreement (kappa value 0.6-0.8). A retrospective cohort was identified from 6 Sydney hospitals between 1972-2003 and archival formalin fixed tissue was collected together with clinicopathological data. Three manual stepwise regression models were fitted for overall, disease-specific and relapse-free survival to determine the value of significant prognostic variables in risk stratification. The models were fitted in a logical order using a careful strategy with step by step interpretation of the results. Immunohistochemistry demonstrated increased sFRP4 membranous expression (> 10%) in 49/95 PC specimens and this correlated with improved overall survival (HR:0.99;95%CI:0.97-6.40;LRchi2=134.75; 1df; ??< 0.001). Increased sFRP4 cytoplasmic staining (> 2/3) in 46/85 patients increased the disease-specific survival (HR:0.52;95%CI:0.31-0.89;LR test statistic =248.40;1df;??< 0.001). Increasing ??-catenin membranous expression (< _60%) in 26/116 patients was associated with an increased risk of overall death (HR:3.18;95%CI:1.14-8.89;LR test statistic =4.61;1df,??< 0.05). Increasing cytoplasmic expression in 65/114 patients was protective and was associated with prolonged survival on univariate, but not multivariate analysis (Disease specific survival HR:0.75;95%CI:0.56-1.00;logrank chi2=3.91;1df; ??=0.05). Increased nuclear ??-catenin expression in 65/114 patients was associated with prolonged survival (disease-specific HR:0.92;95%CI:0.83-1.02; LR test statistic= 49.72;1df;??< 0.001). At the conclusion, 12 patients (8.6%) remained alive, 122 died of their disease (68 males versus 54 females). They were followed for a median of 8.7 months (range 1.0-131.3) months. The median age was 66.5 years (range 34.4-96.0, standard deviation 10.9) years. Pancreatic resection was achieved in 79 patients with 46.8% achieving RO resection. The 30 day post-operative mortality was 2.1%. The overall 1 year survival rate was (33.7% ; 95%CI: 25.78-33.79) with a 5 year survival of (2.87%, 95%CI: 2.83-6.01) and a median survival of (8.90 months; 95%CI: 7.5-10.2). The median disease-specific survival was (9.40; 95%CI: 7.9-10.5 months) and the median time to relapse was 1.2 months (95%CI 1.0-1.2 months). A central tenet of contemporary cancer research is that an understanding of the genetic and molecular abnormalities that accompany the development and progression of cancer is critical to further advances in diagnosis, treatment and eventual prevention. High throughput tissue microarrays were used to study expression of two novel tumour markers in a cohort of pancreatic cancer patients and identified sFRP4 and ??-catenin as potential novel prognostic markers.
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Jinnah, Ali. "Inference for Cox's regression model via a new version of empirical likelihood." unrestricted, 2007. http://etd.gsu.edu/theses/available/etd-11272007-223933/.
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Thesis (M.S.)--Georgia State University, 2007.Title from file title page. Yichuan Zhao, committee chair; Yu-Sheng Hsu , Xu Zhang, Yuanhui Xiao , committee members. Electronic text (54 p.) : digital, PDF file. Description based on contents viewed Feb. 25, 2008. Includes bibliographical references (p. 30-32).
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Erich, Roger Alan. "Regression Modeling of Time to Event Data Using the Ornstein-Uhlenbeck Process." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1342796812.
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Race, Jonathan Andrew. "Semi-parametric Survival Analysis via Dirichlet Process Mixtures of the First Hitting Time Model." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu157357742741077.
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GHASEMI, ABOLFAZL. "Application of Survival Analysis in Forecasting Medical Students at Risk." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1535107693904394.
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Han, Guangming. "Prevalence of Chronic Diseases and Risk Factors for Death among Elderly Americans." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/108.
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The main aim of this study is to explore the effects of risk factors contributing to death in the elderly American population. To achieve this purpose, we constructed Cox proportional hazard regression models and logistic regression models with the complex survey dataset from the national Second Longitudinal Study of Aging (LSOA II) to calculate the hazard ratios (HR)/odds ratios (OR) and confidence interval (CI) of risk factors. Our results show that in addition to chronic disease conditions, many risk factors, such as demographic factors (gender and age), social factors (interaction with friends or relatives), personal health behaviors (smoking and exercise), and biomedical factors (Body mass index and emotional factors) have significant effects on death in the elderly American population. This will provide important information for elderly people to prolong lifespan regardless of whether they have chronic disease/diseases or not.
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Zhao, Meng. "Treatment Comparison in Biomedical Studies Using Survival Function." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_diss/4.
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In the dissertation, we study the statistical evaluation of treatment comparisons by evaluating the relative comparison of survival experiences between two treatment groups. We construct confidence interval and simultaneous confidence bands for the ratio and odds ratio of two survival functions through both parametric and nonparametric approaches.We first construct empirical likelihood confidence interval and simultaneous confidence bands for the odds ratio of two survival functions to address small sample efficacy and sufficiency. The empirical log-likelihood ratio is developed, and the corresponding asymptotic distribution is derived. Simulation studies show that the proposed empirical likelihood band has outperformed the normal approximation band in small sample size cases in the sense that it yields closer coverage probabilities to chosen nominal levels.Furthermore, in order to incorporate prognostic factors for the adjustment of survival functions in the comparison, we construct simultaneous confidence bands for the ratio and odds ratio of survival functions based on both the Cox model and the additive risk model. We develop simultaneous confidence bands by approximating the limiting distribution of cumulative hazard functions by zero-mean Gaussian processes whose distributions can be generated through Monte Carlo simulations. Simulation studies are conducted to evaluate the performance for proposed models. Real applications on published clinical trial data sets are also studied for further illustration purposes.In the end, the population attributable fraction function is studied to measure the impact of risk factors on disease incidence in the population. We develop semiparametric estimation of attributable fraction functions for cohort studies with potentially censored event time under the additive risk model.
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Devamitta, Perera Muditha V. "Statistical Analysis and Modeling of Ovarian and Breast Cancer." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7395.
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The objective of the present study is to investigate key aspects of ovarian and breast cancers, which are two main causes of mortality among women. Identification of the true behavior of survivorship and influential risk factors is essential in designing treatment protocols, increasing disease awareness and preventing possible causes of disease. There is a commonly held belief that African Americans have a higher risk of cancer mortality. We studied racial disparities of women diagnosed with ovarian cancer on overall and disease-free survival and found out that there is no significant difference in the survival experience among the three races: Whites, African Americans and Other races. Tumor sizes at diagnosis among the races were significantly different, as African American women tend to have larger ovarian tumor sizes at the diagnosis. Prognostic models play a major role in health data research. They can be used to estimate adjusted survival probabilities and absolute and relative risks, and to determine significantly contributing risk factors. A prognostic model will be a valuable tool only if it is developed carefully, evaluating the underlying model assumptions and inadequacies and determining if the most relevant model to address the study objectives is selected. In the present study we developed such statistical models for survival data of ovarian and breast cancers. We found that the histology of ovarian cancer had risk ratios that vary over time. We built two types of parametric models to estimate absolute risks and survival probabilities and to adjust the time dependency of the relative risk of Histology. One parametric model is based on classical probability distributions and the other is a more flexible parametric model that estimates the baseline cumulative hazard function using spline functions. In contrast to women diagnosed with ovarian cancer, women with breast cancer showed significantly different survivorship among races where Whites had a poorer overall survival rate compared to African Americans and Other races. In the breast cancer study, we identified that age and progesterone receptor status have time dependent hazard ratios and age and tumor size display non-linear effects on the hazard. We adjusted those non-proportional hazards and non-linear effects by using an extended Cox regression model in order to generate more meaningful interpretations of the data.
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Daly, Kathleen Julia Rose. "Reduction in mortality after inappropriate early discharge from intensive care : logistic regression triage model to predict survival after discharge from intensive care." Thesis, King's College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406755.
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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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Lee, Kyeong Eun. "Bayesian models for DNA microarray data analysis." Diss., Texas A&M University, 2005. http://hdl.handle.net/1969.1/2465.
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Selection of signi?cant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of variables (genes), the selection process can be unstable. This research proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables in a regression setting and use a Bayesian mixture prior to perform the variable selection. Due to the binary nature of the data, the posterior distributions of the parameters are not in explicit form, and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the posterior distributions. The Bayesian model is ?exible enough to identify the signi?cant genes as well as to perform future predictions. The method is applied to cancer classi?cation via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify the set of signi?cant genes to classify BRCA1 and others. Microarray data can also be applied to survival models. We address the issue of how to reduce the dimension in building model by selecting signi?cant genes as well as assessing the estimated survival curves. Additionally, we consider the wellknown Weibull regression and semiparametric proportional hazards (PH) models for survival analysis. With microarray data, we need to consider the case where the number of covariates p exceeds the number of samples n. Speci?cally, for a given vector of response values, which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the responsible genes, which are controlling the survival time. This approach enables us to estimate the survival curve when n << p. In our approach, rather than ?xing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional ?exibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in e?ect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology with (a) di?use large B??cell lymphoma (DLBCL) complementary DNA (cDNA) data and (b) Breast Carcinoma data. Lastly, we propose a mixture of Dirichlet process models using discrete wavelet transform for a curve clustering. In order to characterize these time??course gene expresssions, we consider them as trajectory functions of time and gene??speci?c parameters and obtain their wavelet coe?cients by a discrete wavelet transform. We then build cluster curves using a mixture of Dirichlet process priors.
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Wang, Jiajia. "Assessment of the effects of maternal exposure to heatwave on birth outcomes in Brisbane, Australia." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/76289/1/Jiajia_Wang_Thesis.pdf.
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Even though heatwave events have become more frequent and intense in most regions around the world, little is known about the impact of heatwave on birth outcomes. This thesis uses a population-based study design to investigate the relationship between maternal heatwave exposure and adverse birth outcomes in Brisbane, Australia. This study found that heatwave exposure at any stage of pregnancy can be harmful to fetal growth, and further increase the risk of adverse birth outcomes. Both short- and long-term effects of heatwave on adverse birth outcomes were found. The findings in this thesis may have significant public health implications.
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Zhao, Feng. "Bootstrap variable selection and model validation for Cox's proportional hazards regression models, with applications to the identification of factors predictive of overall and post-relapse survival in advanced epithelial ovarian cancer." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0026/MQ31275.pdf.
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Sauls, Beverly J. "Relative Survival of Gags Mycteroperca microlepis Released Within a Recreational Hook-and-Line Fishery| Application of the Cox Regression Model to Control for Heterogeneity in a Large-Scale Mark-Recapture Study." Thesis, University of South Florida, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1548780.
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The objectives of this study were to measure injuries and impairments directly observed from gags Mycteroperca microlepis caught and released within a large-scale recreational fishery, develop methods that may be used to rapidly assess the condition of reef fish discards, and estimate the total portion of discards in the fishery that suffer latent mortality. Fishery observers were placed on for-hire charter and headboat vessels operating in the Gulf of Mexico from June 2009 through December 2012 to directly observe reef fishes as they were caught by recreational anglers fishing with hook-and-line gear. Fish that were not retained by anglers were inspected and marked with conventional tags prior to release. Fish were released in multiple regions over a large geographic area throughout the year and over multiple years. The majority of recaptured fish were reported by recreational and commercial fishers, and fishing effort fluctuated both spatially and temporally over the course of this study in response to changes in recreational harvest restrictions and the Deepwater Horizon oil spill. Therefore, it could not be assumed that encounter probabilities were equal for all individual tagged fish in the population. Fish size and capture depth when fish were initially caught-and-released also varied among individuals in the study and potentially influenced recapture reporting probabilities. The Cox proportional hazards regression model was used to control for potential covariates on both the occurrence and timing of recapture reporting events so that relative survival among fish released in various conditions could be compared. A total of 3,954 gags were observed in this study, and the majority (77.26%) were released in good condition (condition category 1), defined as fish that immediately submerged without assistance from venting and had not suffered internal injuries from embedded hooks or visible damage to the gills. However, compared to gags caught in shallower depths, a greater proportion of gags caught and released from depths deeper than 30 meters were in fair or poor condition. Relative survival was significantly reduced (alpha <0.05) for gags released in fair and poor condition after controlling for variable mark-recapture reporting rates for different sized discards among regions and across months and years when individual fish were initially captured, tagged and released. Gags released within the recreational fishery in fair and poor condition were 66.4% (95% C.I. 46.9 to 94.0%) and 50.6% (26.2 to 97.8%) as likely to be recaptured, respectively, as gags released in good condition. Overall discard mortality was calculated for gags released in all condition categories at ten meter depth intervals. There was a significant linear increase in estimated mortality from less than 15% (range of uncertainty, 0.1-25.2%) in shallow depths up to 30 meters, to 35.6% (5.6-55.7%) at depths greater than 70 meters (p < 0.001, R2 = 0.917). This analysis demonstrated the utility of the proportional hazards regression model for controlling for potential covariates on both the occurrence and timing of recapture events in a large-scale mark-recapture study and for detecting significant differences in the relative survival of fish released in various conditions measured under highly variable conditions within a large-scale fishery.
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Sauls, Beverly J. "Relative Survival of Gags Mycteroperca microlepis Released Within a Recreational Hook-and-Line Fishery: Application of the Cox Regression Model to Control for Heterogeneity in a Large-Scale Mark-Recapture Study." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4940.
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The objectives of this study were to measure injuries and impairments directly observed from gags Mycteroperca microlepis caught and released within a large-scale recreational fishery, develop methods that may be used to rapidly assess the condition of reef fish discards, and estimate the total portion of discards in the fishery that suffer latent mortality. Fishery observers were placed on for-hire charter and headboat vessels operating in the Gulf of Mexico from June 2009 through December 2012 to directly observe reef fishes as they were caught by recreational anglers fishing with hook-and-line gear. Fish that were not retained by anglers were inspected and marked with conventional tags prior to release. Fish were released in multiple regions over a large geographic area throughout the year and over multiple years. The majority of recaptured fish were reported by recreational and commercial fishers, and fishing effort fluctuated both spatially and temporally over the course of this study in response to changes in recreational harvest restrictions and the Deepwater Horizon oil spill. Therefore, it could not be assumed that encounter probabilities were equal for all individual tagged fish in the population. Fish size and capture depth when fish were initially caught-and-released also varied among individuals in the study and potentially influenced recapture reporting probabilities. The Cox proportional hazards regression model was used to control for potential covariates on both the occurrence and timing of recapture reporting events so that relative survival among fish released in various conditions could be compared.
A total of 3,954 gags were observed in this study, and the majority (77.26%) were released in good condition (condition category 1), defined as fish that immediately submerged without assistance from venting and had not suffered internal injuries from embedded hooks or visible damage to the gills. However, compared to gags caught in shallower depths, a greater proportion of gags caught and released from depths deeper than 30 meters were in fair or poor condition. Relative survival was significantly reduced (alpha (underline)<(/underline)0.05) for gags released in fair and poor condition after controlling for variable mark-recapture reporting rates for different sized discards among regions and across months and years when individual fish were initially captured, tagged and released. Gags released within the recreational fishery in fair and poor condition were 66.4% (95% C.I. 46.9 to 94.0%) and 50.6% (26.2 to 97.8%) as likely to be recaptured, respectively, as gags released in good condition. Overall discard mortality was calculated for gags released in all condition categories at ten meter depth intervals. There was a significant linear increase in estimated mortality from less than 15% (range of uncertainty, 0.1-25.2%) in shallow depths up to 30 meters, to 35.6% (5.6-55.7%) at depths greater than 70 meters (p < 0.001, R2 = 0.917). This analysis demonstrated the utility of the proportional hazards regression model for controlling for potential covariates on both the occurrence and timing of recapture events in a large-scale mark-recapture study and for detecting significant differences in the relative survival of fish released in various conditions measured under highly variable conditions within a large-scale fishery.
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SILVA, Dâmocles Aurélio Nascimento da. "Uma abordagem Bayesiana para análise de sobrevivência de clones de eucaliptos no pólo gesseiro do Araripe-PE." Universidade Federal Rural de Pernambuco, 2006. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/4866.
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Aiming at to contribute, as alternative to minimize the resources of impacts,mainly, for the search of combustible material to take care of the energy demand of the Brazilian half-barren region, we use the techniques of analysis of survival for understanding of a forest of eucalyptus to the long one of the time, and with this to ration the use wooden as combustible for ceramics, bakeries and existing calcinatory of plaster in region. The data given proceeding from a transversal study of 1500 cells of eucalyptus, divided in 4 stratus, taking as base the period of 03/2002 to 09/2004. The graph of probability was used initially for, being based on the test of Anderson-Darling, takes the decision of which function of probability would use in such a way in the classic study as in the Bayesian boarding. A time taken to the decision of choice of the probability distribution, we use the method of Kaplan-Meier and the Actuarial method (life table) to determine the estimates of the parameters and the test distribution free log-rank to test if the curves of the function of probability differed between categories from one same one variable. We used this test to the level of significance of 5%. For these analyses, it was used statistical software Minitab 13 version and statistical package SAS.In the Bayesian boarding was used method Carlo the Mount Chain of Markov (MCMC) for estimate of parameters, using as priori the distribution gamma,found in literature as the distribution that more good was adjusted for biological data and as function of density, used it of the Weibull distribution, chosen as of the better adjustment to the data according to test of Anderson-darling. For thisanalysis software Winbugs 1.4 was used. The results how much to the analysis of the parameters they had indicated that the joined estimates had been closed, same using distinct methods of estimation. As much was concluded that the best distribution to analyze the population in question is the Weibull, according to test of Anderson-Darling and as method for estimation of the parameters of the distribution, the classic method,how much the Bayesian method, reveals good estimators, verified for the amplitude of the intervals reliable 95%. In face of the results, we conclude that if it must have one better control of the eucalyptus, in first the six months of the plantation.
Visando contribuir, como alternativa para minimizar os impactos antrópicos de caracter negativo causado, principalmente, pela busca de material combustível para atender a demanda energética da região semi-árida brasileira, utilizamos as técnicas de análise de sobrevivência para compreensão do comportamento de uma floresta de eucaliptos ao longo do tempo, e com isto racionar o uso de madeira como combustível por cerâmicas, padarias, casas de farinha e calcinadoras de gesso existentes na região. Usaremos dados provenientes de um estudo transversal de 1500 células de eucaliptos, dividido em 4 estratos, tomando como base o período de 03/2002 a 09/2004.Utilizou-se inicialmente o gráfico de probabilidade para, baseado no teste de Anderson-Darling, tomarmos a decisão de qual função de probabilidade utilizaríamos tanto no estudo clássico como na abordagem bayesiana. Uma vez tomada a decisão de escolha da distribuição de probabilidade, utilizamos o método de Kaplan-Meier e o método Atuarial (tábua de vida) para estimativa dos parâmetros e o teste não paramétrico log-rank para testar se as curvas da função de probabilidade diferiam entre categorias de uma mesma variável. Utilizamos esse teste ao nível de significância de 0,05. Para essas análises, foi utilizado o software estatístico Minitab versão 13 e o pacote estatístico SAS.Na abordagem bayesiana utilizou-se a o método de Monte Carlo Cadeia de Markov (MCMC) para estimativa dos parâmetros, utilizando como priori a distribuição gamma, encontrada na literatura como a distribuição que melhor adequa-se para dados biológicos e como função de densidade, utilizou-se a da distribuição Weibull, escolhida como a de melhor ajuste as dados segundo o teste de Anderson-Darling. Para essa análise foi utilizado o Winbugs 1.4.Os resultados quanto a análise dos parâmetros indicaram que as estimativas encontradas foram próxima, mesmo utilizando métodos de estimação distintos. Conclui-se que a melhor distribuição para analisar a população em questão é a Weibull, segundo o teste de Anderson-Darling e como método para estimação dos parâmetros da distribuição, tanto o método clássico, quanto o método bayesiano, mostram-se bons estimadores, verificado pela amplitude dos intervalos de confiança a 95%. Em face dos resultados, concluímos que deve-seter um melhor controle dos eucaliptos, nos primeiros 6 meses de plantio.
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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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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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Konrath, Susanne. "Bayesian regularization in regression models for survival data." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-159745.
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This thesis is concerned with the development of flexible continuous-time survival models based on the accelerated failure time (AFT) model for the survival time and the Cox relative risk (CRR) model for the hazard rate. The flexibility concerns on the one hand the extension of the predictor to take into account simultaneously for a variety of different forms of covariate effects. On the other hand, the often too restrictive parametric assumptions about the survival distribution are replaced by semiparametric approaches that allow very flexible shapes of survival distribution. We use the Bayesian methodology for inference. The arising problems, like e. g. the penalization of high-dimensional linear covariate effects, the smoothing of nonlinear effects as well as the smoothing of the baseline survival distribution, are solved with the application of regularization priors tailored for the respective demand.
The considered expansion of the two survival model classes enables to deal with various challenges arising in practical analysis of survival data. For example the models can deal with high-dimensional feature spaces (e. g. gene expression data), they facilitate feature selection from the whole set or a subset of the available covariates and enable the simultaneous modeling of any type of nonlinear covariate effects for covariates that should always be included in the model. The option of the nonlinear modeling of covariate effects as well as the semiparametric modeling of the survival time distribution enables furthermore also a visual inspection of the linearity assumptions about the covariate effects or accordingly parametric assumptions about the survival time distribution.
In this thesis it is shown, how the p>n paradigm, feature relevance, semiparametric inference for functional effect forms and the semiparametric inference for the survival distribution can be treated within a unified Bayesian framework. Due the option to control the amount of regularization of the considered priors for the linear regression coefficients, there is no need to distinguish conceptionally between the cases p<=n and p>n. To accomplish the desired regularization, the regression coefficients are associated with shrinkage, selection or smoothing priors. Since the utilized regularization priors all facilitate a hierarchical representation, the resulting modular prior structure, in combination with adequate independence assumptions for the prior parameters, enables to establish a unified framework and the possibility to construct efficient MCMC sampling schemes for joint shrinkage, selection and smoothing in flexible classes of survival models. The Bayesian formulation enables therefore the simultaneous estimation of all parameters involved in the models as well as prediction and uncertainty statements about model specification.
The presented methods are inspired from the flexible and general approach for structured additive regression (STAR) for responses from an exponential family and CRR-type survival models. Such systematic and flexible extensions are in general not available for AFT models. An aim of this work is to extend the class of AFT models in order to provide such a rich class of models as resulting from the STAR approach, where the main focus relies on the shrinkage of linear effects, the selection of covariates with linear effects together with the smoothing of nonlinear effects of continuous covariates as representative of a nonlinear modeling. Combined are in particular the Bayesian lasso, the Bayesian ridge and the Bayesian NMIG (a kind of spike-and-slab prior) approach to regularize the linear effects and the P-spline approach to regularize the smoothness of the nonlinear effects and the baseline survival time distribution. To model a flexible error distribution for the AFT model, the parametric assumption for the baseline error distribution is replaced by the assumption of a finite Gaussian mixture distribution. For the special case of specifying one basis mixture component the estimation problem essentially boils down to estimation of log-normal AFT model with STAR predictor. In addition, the existing class of CRR survival models with STAR predictor, where also baseline hazard rate is approximated by a P-spline, is expanded to enable the regularization of the linear effects with the mentioned priors, which broadens further the area of application of this rich class of CRR models. Finally, the combined shrinkage, selection and smoothing approach is also introduced to the semiparametric version of the CRR model, where the baseline hazard is unspecified and inference is based on the partial likelihood.
Besides the extension of the two survival model classes the different regularization properties of the considered shrinkage and selection priors are examined. The developed methods and algorithms are implemented in the public available software BayesX and in R-functions and the performance of the methods and algorithms is extensively tested by simulation studies and illustrated through three real world data sets.
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Ye, Hong. "Comparison of Cox regression and discrete time survival models." Thesis, Wayne State University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10153426.
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A standard analysis of prostate cancer biochemical failure data is done by conducting two approaches in which risk factors or covariates are measured. Cox regression and discrete-time survival models were compared under different attributes: sample size, time periods, and parameters in the model. The person-period data was reconstructed when examining the same data in discrete-time survival model. Twenty-four numerical examples covering a variety of sample sizes, time periods, and number of parameters displayed the closeness of Cox regression and discrete-time survival methods in situations typical of the cancer study.
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Masiulaitytė, Inga. "Regression and degradation models in reliability theory and survival analysis." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100527_134956-15325.
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In doctoral thesis redundant systems and degradation models are considered. To ensure high reliability of important elements of the system, the stand-by units can be used. These units are commuted and operate instead of the main failed unit. The stand-by units can function in the different conditions: “hot”, “cold” or “warm” reserving. In the thesis systems with “warm” stand-by units are analyzed. Hypotheses of smooth commuting are formulated and goodness-of-fit tests for these hypotheses are constructed. Nonparametric and parametric point and interval estimation procedures are given. Modeling and statistical estimation of reliability of systems from failure time and degradation data are considered.Daktaro disertacijos tyrimo objektai yra rezervuotos sistemos ir degradaciniai modeliai. Norint užtikrinti svarbių sistemos elementų aukštą patikimumą, naudojami jų rezerviniai elementai, kurie gali būti įjungiami sugedus šiems pagrindiniams elementams. Rezerviniai elementai gali funkcionuoti skirtinguose režimuose: „karštame“, „šaltame“ arba „šiltame“. Disertacijoje yra nagrinėjamos sistemos su „šiltai“ rezervuotais elementais. Darbe suformuluojama rezervinio elemento „sklandaus įjungimo“ hipotezė ir konstruojami statistiniai kriterijai šiai hipotezei tikrinti. Nagrinėjami neparametrinio ir parametrinio taškinio bei intervalinio vertinimo uždaviniai. Disertacijoje nagrinėjami pakankamai bendri degradacijos modeliai, kurie aprašo elementų gedimų intensyvumą kaip funkciją kiek naudojamų apkrovų, tiek ir degradacijos lygio, kuri savo ruožtu modeliuojama naudojant stochastinius procesus.
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鄧雅恩 and Nga-yan Fancy Tang. "Resampling tests for some survival models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226759.
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Tang, Nga-yan Fancy. "Resampling tests for some survival models /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23457338.
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Pescim, Rodrigo Rossetto. "A distribuição beta generalizada semi-normal." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-25022010-103042/.
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Uma nova família de distribuições denominada distribuição beta generalizada semi-normal, que inclui algumas distribuições importantes como casos especiais, tais como as distribuições semi-normal e generalizada semi-normal (Cooray e Ananda, 2008), é proposta neste trabalho. Para essa nova família de distribuições, foi realizado o estudo da função densidade probabilidade, função de distribuição acumulada e da função de taxa de falha (ou risco), que não dependeram de funções matemáticas complicadas. Obteve-se uma expressão formal para os momentos, função geradora de momentos, função densidade da distribuição de estatística de ordem, desvios médios, entropia, contabilidade e para as curvas de Bonferroni e Lorenz. Examinaram-se os estimadores de máxima verossimilhança dos parâmetros e deduziu- se a matriz de informação esperada. Neste trabalho é proposto, também, um modelo de regressão utilizando a distribuição beta generalizada semi-normal. A utilidade dessa nova distribuição é ilustrada através de dois conjuntos de dados, mostrando que ela é mais flexível na análise de dados de tempo de vida do que outras distribuições existentes na literatura.A new family of distributions so-called beta generalized half-normal distribution, which includes some important distributions as special cases, such as the half-normal and generalized half-normal (Cooray and Ananda, 2008) distributions, is proposed in this work. For this new family of distributions, we studied the probability density function, cumulative distribution function and failure rate function (or hazard function), which did not depend on complicated mathematical functions. We obtained a formal expression for the moments, moment generating function, density function of order statistics distribution, mean deviation, entropy, reliability and Bonferroni and Lorenz curves. We examined maximum likelihood estimation of parameters and provided the information matrix. This work also proposed a regression model using the beta generalized half-normal distribution. The usefulness of the new distribution is illustrated through two data sets by showing that it is quite °exible in analyzing lifetime data instead other distributions in the literature.
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Rathnayake, Rasanji Chathumali. "Inference for Some GLMs and Survival Regression Models after Variable Selection." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/dissertations/1676.
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Consider a regression model where Y|x ∼ D(h(x),θ) for some real valued function such as h(x) = xTβ where D is a parametric distribution that depends on x only through h(x). Several important generalized linear models, generalized additive models, and survival regression models have this form. To obtain a prediction interval for a future value of the response variable Yf given a vector of predictors xf, apply the nonparametric shorth prediction interval to Y ∗ 1 ,...,Y ∗ B where the Y ∗ i are independent and identically distributed from the distribution D(ˆ h(xf), ˆ θ). These prediction intervals can also work after variable or model selection.
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Goungounga, Juste Aristide. "Extention de l'analyse de la survie nette au domaine de la recherche clinique." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0715.
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La survie nette est un indicateur incontournable pour juger du control du cancer. Par définition, elle correspond à la survie que l’on observerait dans un monde hypothétique où le cancer étudié serait la seule cause possible de décès. L’objectif principal de cette thèse était de montrer l’intérêt de cet indicateur dans le cadre de la recherche clinique en prenant en compte quelques défis méthodologiques qui peuvent être rencontrés dans ce contexte. Nous avons présenté d’abord le concept de survie nette et ses méthodes d’estimation. Par la suite nous nous sommes intéressés à quelques problématiques rencontrées dans les essais cliniques à long terme lorsque l’on s’intéresse à l’estimation de la survie nette. Nous avons étudié également l’impact de l’utilisation de l’approche classique d’estimation de la survie nette dans les essais cliniques, i.e. la méthode cause-spécifique dans différentes configurations d’erreurs de classifications de la cause de décès. La deuxième problématique de cette thèse a porté sur la prise en compte du biais de sélection en termes de mortalité autres causes des patients. Nous avons proposé un modèle de mortalité en excès prenant en compte ce type de biais de sélection. Une troisième problématique qui est complémentaire à la deuxième est de prendre en compte inter-centres en même temps que le biais de sélection. Ce travail propose ainsi de nouveaux outils pouvant aider les spécialistes de la recherche clinique à évaluer de nouvelles stratégies thérapeutiques dans les essais cliniques en cancérologie, mais aussi dans d’autres domaines cliniques d’applicationsNet survival is a key indicator for measuring cancer control. By definition, it corresponds to the survival that would be observed in a hypothetical world where the cancer studied is the only possible cause of death. The main objective of this thesis was to show the interest of this indicator in the context of clinical research taking into account some methodological challenges that can be encountered. In this work, we have first presented the concept of net survival and its estimation methods. Subsequently, we were interested in some of the problems encountered in long-term clinical trials when the interest is in estimating net survival. We studied the impact of using the classic approach when estimating net survival in clinical trials, i.e. the cause-specific method in different configurations of misclassifications of the cause of death. The second objective of this thesis was to take into account the selection bias in terms of other causes mortality in the modeling of excess mortality, because of the noncomparability between patients from general population and those of clinical trials. We proposed an excess hazard model that corrects this type of selection bias. A third problem which is complementary to the second is to take into account the heterogeneity of patients in the different recruitment centers at the same time as the selection bias. This work proposes new tools which can help clinical research specialists to evaluate new therapeutic strategies in cancer clinical trials, but also in other areas of clinical application
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Mello, Joao Fernando Serrajordia Rocha de. "Modelo preditivo para perda de crédito e sua aplicação em decisão de spread." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-27052009-174727/.
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Métodos analíticos para concessão de crédito vêm apresentando enormes avanços nas últimas décadas, particularmente no que se refere a métodos estatísticos de classificação para identificar grupos de indivíduos com diferentes taxas de inadimplência. A maioria dos trabalhos existentes sugere decisões do tipo conceder o crédito ou não, considerando apenas de forma marginal o resultado esperado da operação. O presente trabalho tem o objetivo de propor um modelo de avaliação de risco de crédito mais complexo que os tradicionais modelos de Credit Scoring, que forneça uma perspectiva mais detalhada acerca do desempenho futuro de um contrato de crédito, e que vá além da classificação entre bom e mau pagador. Aliado a este ganho de informação na previsibilidade oferecida pelo modelo, também é objetivo ampliar o espaço de decisões do problema, saindo de uma resposta binária (como aceitar/rejeitar o crédito) para algo que responda à seguinte pergunta: qual é a taxa justa para cobrir determinado risco?.Analytical methods for granting credit are presenting enormous advances in recent decades, particularly in the field of statistical methods of classification to identify groups of individuals with different rates of default. Most of the existing work suggests decisions of the type granting credit or not, regarding just marginally the expected outcome of the operation. This work aims to propose a model to evaluate credit risk with more complexity than the traditional \"Credit Scoring\" models, providing a more detailed view about the future performance of a credit agreement, which goes beyond the classification of good and bad payers. Coupled with this improvement of information offered by the model, it is also this works aim to expand the decision space of the problem, leaving a binary response (such as accept/reject the claim) to something that answers the following question: \"what is the fair rate to cover a given risk \".
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Prataviera, Fábio. "O modelo de regressão odd log-logística gama generalizada com aplicações em análise de sobrevivência." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-26102017-141941/.
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Propor uma família de distribuição de probabilidade mais ampla e flexível é de grande importância em estudos estatísticos. Neste trabalho é utilizado um novo método de adicionar um parâmetro para uma distribuição contínua. A distribuição gama generalizada, que tem como casos especiais a distribuição Weibull, exponencial, gama, qui-quadrado, é usada como distribuição base. O novo modelo obtido tem quatro parâmetros e é chamado odd log-logística gama generalizada (OLLGG). Uma das características interessante do modelo OLLGG é o fato de apresentar bimodalidade. Outra proposta deste trabalho é introduzir um modelo de regressão chamado log-odd log-logística gama generalizada (LOLLGG) com base na GG (Stacy e Mihram, 1965). Este modelo pode ser muito útil, quando por exemplo, os dados amostrados possuem uma mistura de duas populações estatísticas. Outra vantagem da distribuição OLLGG consiste na capacidade de apresentar várias formas para a função de risco, crescente, decrescente, na forma de U e bimodal entre outras. Desta forma, são apresentadas em ambos os casos as expressões explícitas para os momentos, função geradora e desvios médios. Considerando dados nãocensurados e censurados de forma aleatória, as estimativas para os parâmetros de interesse, foram obtidas via método da máxima verossimilhança. Estudos de simulação, considerando diferentes valores para os parâmetros, porcentagens de censura e tamanhos amostrais foram conduzidos com o objetivo de verificar a flexibilidade da distribuição e a adequabilidade dos resíduos no modelo de regressão. Para ilustrar, são realizadas aplicações em conjuntos de dados reais.Providing a wider and more flexible probability distribution family is of great importance in statistical studies. In this work a new method of adding a parameter to a continuous distribution is used. In this study the generalized gamma distribution (GG) is used as base distribution. The GG distribution has, as especial cases, Weibull distribution, exponential, gamma, chi-square, among others. For this motive, it is considered a flexible distribution in data modeling procedures. The new model obtained with four parameters is called log-odd log-logistic generalized gamma (OLLGG). One of the interesting characteristics of the OLLGG model is the fact that it presents bimodality. In addition, a regression model regression model called log-odd log-logistic generalized gamma (LOLLGG) based by GG (Stacy e Mihram, 1965) is introduced. This model can be very useful when, the sampled data has a mixture of two statistical populations. Another advantage of the OLLGG distribution is the ability to present various forms for the failing rate, as increasing, as decreasing, and the shapes of bathtub or U. Explicity expressions for the moments, generating functions, mean deviations are obtained. Considering non-censored and randomly censored data, the estimates for the parameters of interest were obtained using the maximum likelihood method. Simulation studies, considering different values for the parameters, percentages of censoring and sample sizes were done in order to verify the distribuition flexibility, and the residues distrbutuon in the regression model. To illustrate, some applications using real data sets are carried out.
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Konrath, Susanne [Verfasser], and Ludwig [Akademischer Betreuer] Fahrmeir. "Bayesian regularization in regression models for survival data / Susanne Konrath. Betreuer: Ludwig Fahrmeir." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2013. http://d-nb.info/1038704197/34.
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Konrath, Susanne Verfasser], and Ludwig [Akademischer Betreuer] [Fahrmeir. "Bayesian regularization in regression models for survival data / Susanne Konrath. Betreuer: Ludwig Fahrmeir." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-159745.
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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 maintenance. Optimal times are calculated with respect to the minimization of a cost function which considers expected costs of preventive and corrective maintenance. Another aim is the investigation of the effect of five different failure modes on the component lifetime. The methods used are based on the Weibull distribution, in particular we apply the Bayesian Weibull AFT model and the Bayesian Generalized Weibull model. The latter is preferable for its greater flexibility and better performance. Results reveal that components from gas turbines located in a heavy industrial environment at a higher distance from sea tend to have shorter lifetime. Then, failure mode A seems to be the most harmful for the component lifetime. The model used is capable of predicting customer-specific optimal replacement times based on the effect of environmental attributes. Predictions can be also extended for new components installed at new customer sites.
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Lam, Chung-sang, and 林仲生. "Survival analysis of the timing of goals in soccer games." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B32028635.
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Jardillier, Rémy. "Evaluation de différentes variantes du modèle de Cox pour le pronostic de patients atteints de cancer à partir de données publiques de séquençage et cliniques." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALS008.
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Le cancer constitue la première cause de mortalité prématurée (décès avant 65 ans) en France depuis 2004. Pour un même organe, chaque cancer est unique, et le pronostic personnalisé est donc un aspect important de la prise en charge et du suivi des patients. La baisse des coûts du séquençage des ARN a permis de mesurer à large échelle les profils moléculaires de nombreux échantillons tumoraux. Ainsi, la base de données TCGA fournit les données RNA-seq de tumeurs, des données cliniques (âge, sexe, grade, stade, etc.), et les temps de suivi des patients associés sur plusieurs années (dont la survie du patient, la récidive éventuelle, etc.). De nouvelles découvertes sont donc rendues possibles en terme de biomarqueurs construits à partir de données transcriptomiques, avec des pronostics individualisés. Ces avancées requièrent le développement de méthodes d’analyse de données en grande dimension adaptées à la prise en compte à la fois des données de survie (censurées à droite), des caractéristiques cliniques, et des profils moléculaires des patients. Dans ce contexte, l’objet principal de la thèse consiste à comparer et adapter des méthodologies pour construire des scores de risques pronostiques de la survie ou de la récidive des patients atteints de cancer à partir de données de séquençage et cliniques.Le modèle de Cox (semi-paramétrique) est largement utilisé pour modéliser ces données de survie, et permet de les relier à des variables explicatives. Les données RNA-seq de TCGA contiennent plus de 20 000 gènes pour seulement quelques centaines de patients. Le nombre p de variables excède alors le nombre n de patients, et l'estimation des paramètres est soumis à la « malédiction de la dimension ». Les deux principales stratégies permettant de remédier à cela sont les méthodes de pénalisation et le pré-filtrage des gènes. Ainsi, le premier objectif de cette thèse est de comparer les méthodes de pénalisations classiques du modèle de Cox (i.e. ridge, lasso, elastic net, adaptive elastic net). Pour cela, nous utilisons des données réelles et simulées permettant de contrôler la quantité d’information contenue dans les données transcriptomiques. Ensuite, la deuxième problématique abordée concerne le pré-filtrage univarié des gènes avant l’utilisation d’un modèle de Cox multivarié. Nous proposons une méthodologie permettant d’augmenter la stabilité des gènes sélectionnés, et de choisir les seuils de filtrage en optimisant les prédictions. Enfin, bien que le coût du séquençage (RNA-seq) ait diminué drastiquement au cours de la dernière décennie, il reste trop élevé pour une utilisation routinière en pratique. Dans une dernière partie, nous montrons que la profondeur de séquençage des miARN peut être réduite sans atténuer la qualité des prédictions pour certains cancers de TCGA, mais pas pour d’autresCancer has been the leading cause of premature mortality (death before the age of 65) in France since 2004. For the same organ, each cancer is unique, and personalized prognosis is therefore an important aspect of patient management and follow-up. The decrease in sequencing costs over the last decade have made it possible to measure the molecular profiles of many tumors on a large scale. Thus, the TCGA database provides RNA-seq data of tumors, clinical data (age, sex, grade, stage, etc.), and follow-up times of associated patients over several years (including patient survival, possible recurrence, etc.). New discoveries are thus made possible in terms of biomarkers built from transcriptomic data, with individualized prognoses. These advances require the development of large-scale data analysis methods adapted to take into account both survival data (right-censored), clinical characteristics, and molecular profiles of patients. In this context, the main goal of the thesis is to compare and adapt methodologies to construct prognostic risk scores for survival or recurrence of patients with cancer from sequencing and clinical data.The Cox model (semi-parametric) is widely used to model these survival data, and allows linking them to explanatory variables. The RNA-seq data from TCGA contain more than 20,000 genes for only a few hundred patients. The number p of variables then exceeds the number n of patients, and parameters estimation is subject to the “curse of dimensionality”. The two main strategies to overcome this issue are penalty methods and gene pre-filtering. Thus, the first objective of this thesis is to compare the classical penalization methods of Cox's model (i.e. ridge, lasso, elastic net, adaptive elastic net). To this end, we use real and simulated data to control the amount of information contained in the transcriptomic data. Then, the second issue addressed concerns the univariate pre-filtering of genes before using a multivariate Cox model. We propose a methodology to increase the stability of the genes selected, and to choose the filtering thresholds by optimizing the predictions. Finally, although the cost of sequencing (RNA-seq) has decreased drastically over the last decade, it remains too high for routine use in practice. In a final section, we show that the sequencing depth of miRNAs can be reduced without degrading the quality of predictions for some TCGA cancers, but not for others
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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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Zhang, Hanze. "Bayesian inference on quantile regression-based mixed-effects joint models for longitudinal-survival data from AIDS studies." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7456.
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In HIV/AIDS studies, viral load (the number of copies of HIV-1 RNA) and CD4 cell counts are important biomarkers of the severity of viral infection, disease progression, and treatment evaluation. Recently, joint models, which have the capability on the bias reduction and estimates' efficiency improvement, have been developed to assess the longitudinal process, survival process, and the relationship between them simultaneously. However, the majority of the joint models are based on mean regression, which concentrates only on the mean effect of outcome variable conditional on certain covariates. In fact, in HIV/AIDS research, the mean effect may not always be of interest. Additionally, if obvious outliers or heavy tails exist, mean regression model may lead to non-robust results. Moreover, due to some data features, like left-censoring caused by the limit of detection (LOD), covariates with measurement errors and skewness, analysis of such complicated longitudinal and survival data still poses many challenges. Ignoring these data features may result in biased inference.
Compared to the mean regression model, quantile regression (QR) model belongs to a robust model family, which can give a full scan of covariate effect at different quantiles of the response, and may be more robust to extreme values. Also, QR is more flexible, since the distribution of the outcome does not need to be strictly specified as certain parametric assumptions. These advantages make QR be receiving increasing attention in diverse areas. To the best of our knowledge, few study focuses on the QR-based joint models and applies to longitudinal-survival data with multiple features.
Thus, in this dissertation research, we firstly developed three QR-based joint models via Bayesian inferential approach, including: (i) QR-based nonlinear mixed-effects joint models for longitudinal-survival data with multiple features; (ii) QR-based partially linear mixed-effects joint models for longitudinal data with multiple features; (iii) QR-based partially linear mixed-effects joint models for longitudinal-survival data with multiple features. The proposed joint models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also implemented to assess the performance of the proposed methods under different scenarios. Although this is a biostatistical methodology study, some interesting clinical findings are also discovered.
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Wojciechowska, Sonia. "The conditional control of MITF reveals cellular subpopulations essential for melanoma survival and recurrence in new zebrafish models." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/29645.
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Melanoma is the most lethal type of skin cancer with over 132,000 cases occurring globally each year and continually rising incidence. BRAFV600E inhibitors have led to clinically significant improvements in outcomes for melanoma patients, yet many patients with metastatic melanoma rapidly succumb to the disease due to eventual chemoresistance or insensitivity to the drug. Thus, it is critical to identify new therapies that can act alone, or be combined with available treatments for enhanced efficacy and/or to overcome drug resistance. Evidence from human melanoma indicates that the melanocyte lineage is critical for melanoma survival and contributes to therapeutic resistance. MITF is a highly conserved “master melanocyte transcription factor” with a complex role in melanoma. Our lab has previously developed a temperature sensitive BRAFV600E mitfavc7 zebrafish melanoma model carrying a human oncogene and mitfavc7 splice site mutation that enables the conditional control of its endogenous activity by changes to water temperature. As part of my PhD project, I characterized and compared two new models developed since then: a very aggressive BRAFV600E mitfavc7p53M214K melanoma model with three driving mutations and a slower developing BRAF-independent mitfavc7p53M214K. I showed that the MITF activity is crucial for melanocyte survival in both models and that both mutated BRAF and p53 deficiency are oncogenic with low levels of MITF, and result in fish nevi and melanoma resembling the pathology of human disease. Both models are also relevant to a low-MITF subclass of human melanomas that emerged from a recent classification by The Cancer Genome Atlas Network. In addition, I established that, similarly to the BRAFV600Emitfavc7, complete inhibition of MITF activity leads to rapid tumour regression, but once its activity is restored the melanomas recur at the same site as the original tumour. I used histopathology studies and melanocyte lineage transgenes to identify and visualize subpopulations of cells remaining at the site of regression in these new zebrafish melanoma models. I hypothesised that these are the cells of origin for tumour recurrence (melanoma stem or progenitor cells), showed that some of them express a cancer stem cell marker aldehyde dehydrogenase, and attempted to target these subpopulations using 5-nitrofurans (a prodrug NFN1, shown previously by our lab to target ALDHhigh subpopulations in context of melanoma) in fish after melanoma regression. Finally, I also developed and described a new primary zebrafish melanoma cell line that I derived from one of these zebrafish tumours. This study is still in progress, but the cell line will be a useful tool for further investigation of these proposed melanoma progenitor cells in vitro, with potential applications for lineage tracing and transplantations.
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Junior, Antonio Carlos Ricardo Braga. "Distribuições das classes Kumaraswamy generalizada e exponenciada: propriedades e aplicações." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-07062013-150103/.
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Recentemente, Cordeiro e de Castro (2011) apresentaram uma classe generalizada baseada na distribuição Kumaraswamy (Kw-G). Essa classe de distribuições modela as formas de risco crescente, decrescente, unimodal e forma de U ou de banheira. Uma importante distribuição pertencente a essa classe é a distribuição Kumaraswamy Weibull modificada (KwMW) proposta por Cordeiro; Ortega e Silva (2013). Com isso foi utilizada essa distribuição para o desenvolvimento de algumas novas propriedades e análise bayesiana. Além disso, foi desenvolvida uma nova distribuição de probabilidade a partir da distribuição gama generalizada geométrica (GGG) que foi denominada de gama generalizada geométrica exponenciada (GGGE). Para a nova distribuição GGGE foram calculados os momentos, a função geradora de momentos, os desvios médios, a confiabilidade e as estatísticas de ordem. Desenvolveu-se o modelo de regressão log-gama generalizada geométrica exponenciada. Para a estimação dos parâmetros, foram utilizados os métodos de máxima verossimilhança e bayesiano e, finalmente, para ilustrar a aplicação da nova distribuição foi analisado um conjunto de dados reais.Recently, Cordeiro and de Castro (2011) showed a generalized class based on the Kumaraswamy distribution (Kw-G). This class of models has crescent risk forms, decrescent, unimodal and U or bathtub form. An important distribution belonging to this class the Kumaraswamy modified Weibull distribution (KwMW), proposed by Cordeiro; Ortega e Silva (2013). Thus this distribution was used to develop some new properties and bayesian analysis. Furthermore, we develop a new probability distribution from the generalized gamma geometric distribution (GGG) which it is called generalized gamma geometric exponentiated (GGGE) distribution. For the new distribution we calculate the moments, moment generating function, mean deviation, reliability and order statistics. We define a log-generalized gamma geometric exponentiated regression model. The methods used to estimate the model parameters are: maximum likelihood and bayesian. Finally, we illustrate the potentiality of the new distribution by means of an application to a real data set.
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Barrett, James Edward. "Gaussian process regression models for the analysis of survival data with competing risks, interval censoring and high dimensionality." Thesis, King's College London (University of London), 2015. http://kclpure.kcl.ac.uk/portal/en/theses/gaussian-process-regression-models-for-the-analysis-of-survival-data-with-competing-risks-interval-censoring-and-high-dimensionality(fe3440e1-9766-4fc3-9d23-fe4af89483b5).html.
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We develop novel statistical methods for analysing biomedical survival data based on Gaussian process (GP) regression. GP regression provides a powerful non-parametric probabilistic method of relating inputs to outputs. We apply this to survival data which consist of time-to-event and covariate measurements. In the context of GP regression the covariates are regarded as `inputs' and the event times are the `outputs'. This allows for highly exible inference of non-linear relationships between covariates and event times. Many existing methods for analysing survival data, such as the ubiquitous Cox proportional hazards model, focus primarily on the hazard rate which is typically assumed to take some parametric or semi-parametric form. Our proposed model belongs to the class of accelerated failure time models and as such our focus is on directly characterising the relationship between the covariates and event times without any explicit assumptions on what form the hazard rates take. This provides a more direct route to connecting the covariates to survival outcomes with minimal assumptions. An application of our model to experimental data illustrates its usefulness. We then apply multiple output GP regression, which can handle multiple potentially correlated outputs for each input, to competing risks survival data where multiple event types can occur. In this case the multiple outputs correspond to the time-to-event for each risk. By tuning one of the model parameters we can control the extent to which the multiple outputs are dependent thus allowing the specication of correlated risks. However, the identiability problem, which states that it is not possible to infer whether risks are truly independent or otherwise on the basis of observed data, still holds. In spite of this fundamental limitation simulation studies suggest that in some cases assuming dependence can lead to more accurate predictions. The second part of this thesis is concerned with high dimensional survival data where there are a large number of covariates compared to relatively few individuals. This leads to the problem of overtting, where spurious relationships are inferred from the data. One strategy to tackle this problem is dimensionality reduction. The Gaussian process latent variable model (GPLVM) is a powerful method of extracting a low dimensional representation of high dimensional data. We extend the GPLVM to incorporate survival outcomes by combining the model with a Weibull proportional hazards model (WPHM). By reducing the ratio of covariates to samples we hope to diminish the eects of overtting. The combined GPLVM-WPHM model can also be used to combine several datasets by simultaneously expressing them in terms of the same low dimensional latent variables. We construct the Laplace approximation of the marginal likelihood and use this to determine the optimal number of latent variables, thereby allowing detection of intrinsic low dimensional structure. Results from both simulated and real data show a reduction in overtting and an increase in predictive accuracy after dimensionality reduction.
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Čabla, Adam. "Odhady v analýze přežívání." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-17134.
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This thesis introduces methods used in time-to-date analysis. It is written generally and so usable in dealing with any example. The thesis deals with problem of censoring, which means, that some observations occurred after the following, which is typical for the lifetime analysis. Methods mentioned in the thesis are nonparametric and parametric estimates of the survival function and their characteristics, and regression models, concretely Cox model and accelerated failure time model, which examine effect of the covariates on survival function. In the thesis is beside survival function presented hazard function, which express intensity of the analyzed event and cumulative hazard function, which is created as the name suggests by cumulative summation of the hazard function. Estimates of these functions are obtainable from survival function and for parametric estimate often exists formula resulting from parameters of used distribution. Empirical part of the thesis introduces influence of several different types and degrees of censoring on parametric and nonparametric estimates of the survival function, mean and median. The other empirical example is the usage of regression analysis on the data from the lungs cancer research made by Mayo Clinic.
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Tran, Xuan Quang. "Les modèles de régression dynamique et leurs applications en analyse de survie et fiabilité." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0147/document.
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Cette thèse a été conçu pour explorer les modèles dynamiques de régression, d’évaluer les inférences statistiques pour l’analyse des données de survie et de fiabilité. Ces modèles de régression dynamiques que nous avons considérés, y compris le modèle des hasards proportionnels paramétriques et celui de la vie accélérée avec les variables qui peut-être dépendent du temps. Nous avons discuté des problèmes suivants dans cette thèse.Nous avons présenté tout d’abord une statistique de test du chi-deux généraliséeY2nquiest adaptative pour les données de survie et fiabilité en présence de trois cas, complètes,censurées à droite et censurées à droite avec les covariables. Nous avons présenté en détailla forme pratique deY2nstatistique en analyse des données de survie. Ensuite, nous avons considéré deux modèles paramétriques très flexibles, d’évaluer les significations statistiques pour ces modèles proposées en utilisantY2nstatistique. Ces modèles incluent du modèle de vie accélérés (AFT) et celui de hasards proportionnels (PH) basés sur la distribution de Hypertabastic. Ces deux modèles sont proposés pour étudier la distribution de l’analyse de la duré de survie en comparaison avec d’autre modèles paramétriques. Nous avons validé ces modèles paramétriques en utilisantY2n. Les études de simulation ont été conçus.Dans le dernier chapitre, nous avons proposé les applications de ces modèles paramétriques à trois données de bio-médicale. Le premier a été fait les données étendues des temps de rémission des patients de leucémie aiguë qui ont été proposées par Freireich et al. sur la comparaison de deux groupes de traitement avec des informations supplémentaires sur les log du blanc du nombre de globules. Elle a montré que le modèle Hypertabastic AFT est un modèle précis pour ces données. Le second a été fait sur l’étude de tumeur cérébrale avec les patients de gliome malin, ont été proposées par Sauerbrei & Schumacher. Elle a montré que le meilleur modèle est Hypertabastic PH à l’ajout de cinq variables de signification. La troisième demande a été faite sur les données de Semenova & Bitukov, à concernant les patients de myélome multiple. Nous n’avons pas proposé un modèle exactement pour ces données. En raison de cela était les intersections de temps de survie.Par conséquent, nous vous conseillons d’utiliser un autre modèle dynamique que le modèle de la Simple Cross-Effect à installer ces donnéesThis thesis was designed to explore the dynamic regression models, assessing the sta-tistical inference for the survival and reliability data analysis. These dynamic regressionmodels that we have been considered including the parametric proportional hazards andaccelerated failure time models contain the possibly time-dependent covariates. We dis-cussed the following problems in this thesis.At first, we presented a generalized chi-squared test statisticsY2nthat is a convenient tofit the survival and reliability data analysis in presence of three cases: complete, censoredand censored with covariates. We described in detail the theory and the mechanism to usedofY2ntest statistic in the survival and reliability data analysis. Next, we considered theflexible parametric models, evaluating the statistical significance of them by usingY2nandlog-likelihood test statistics. These parametric models include the accelerated failure time(AFT) and a proportional hazards (PH) models based on the Hypertabastic distribution.These two models are proposed to investigate the distribution of the survival and reliabilitydata in comparison with some other parametric models. The simulation studies were de-signed, to demonstrate the asymptotically normally distributed of the maximum likelihood estimators of Hypertabastic’s parameter, to validate of the asymptotically property of Y2n test statistic for Hypertabastic distribution when the right censoring probability equal 0% and 20%.n the last chapter, we applied those two parametric models above to three scenes ofthe real-life data. The first one was done the data set given by Freireich et al. on thecomparison of two treatment groups with additional information about log white blood cellcount, to test the ability of a therapy to prolong the remission times of the acute leukemiapatients. It showed that Hypertabastic AFT model is an accurate model for this dataset.The second one was done on the brain tumour study with malignant glioma patients, givenby Sauerbrei & Schumacher. It showed that the best model is Hypertabastic PH onadding five significance covariates. The third application was done on the data set given by Semenova & Bitukov on the survival times of the multiple myeloma patients. We did not propose an exactly model for this dataset. Because of that was an existing oneintersection of survival times. We, therefore, suggest fitting other dynamic model as SimpleCross-Effect model for this dataset
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Fogo, José Carlos. "Modelo de regressão para um processo de renovação Weibull com termo de fragilidade." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-26092007-104428/.
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Processsos de renovação são um caso especial de processos pontuais envolvendo eventos recorrentes nos quais um item ou unidade, após a ocorrência de uma falha, é recolocado na mesma condição de novo. Devido a essa propriedade os tempos entre ocorrências para um processo de renovação são independentes e a sua função intensidade é dada pela função de risco. Fatores que interferem nos tempos de recorrência de unidades distintas, ou indivíduos, e que não são observados, podem ser modelados com a inclusão de um termo de fragilidade no modelo. Neste trabalho é apresentado o desenvolvimento de um modelo de regressão para um processo de renovação com tempos entre ocorrências com distribuição de Weibull. Na modelagem foi considerada, ainda, a presença de censuras e a inclusão de um termo de fragilidade para explicar a relação existente entre os tempos de recorrências de uma unidade. A metodologia é desenvolvida para o caso em que várias unidades são acometidas por eventos recorrentes. Nas simulações realizadas foram analisadas as probabilidades de cobertura empíricas do intervalo de confiança normal assintótico e também o comportamento das variâncias dos estimadores. A presença de censuras na amostra inflacionou as variâncias dos estimadores de máxima verossimilhança além de produzir estimativas viciadas para um dos parâmetros da regressão, sendo que o vício do estimador foi corrigido por meio de um processo "bootstrap". Na modelagem sem termo de fragilidade, os resultados das análises das probabilidades de cobertura empírica dos intervalos de confiança assintóticos mostraram uma boa aproximação com os valores esperados, mas com certos cuidados a serem tomados, especialmente nos procedimentos baseados na simetria das distribuições empíricas. A inclusão de um termo de fragilidade na modelagem, por sua vez, causou uma perturbação na estimação máxima verossimilhança com um aumento nas variâncias dos estimadores diretamente associados à variabilidade do termo de fragilidade. Além disso, as coberturas empíricas dos intervalos de confiança assintóticos foram, na grande maioria superestimadas, com resultados satisfatórios apenas para o parâmetro de forma da distribuição Weibull.Renewal Processes are a special case of point processes involving recurrent events in which a unit, after a failure, is restored to the like new condition. Due to that property the times between occurrences for a renewal process are independent and its intensity function is given by the hazard function. Random factors not observed, that afects the recurrence times of the units, can be explained by a frailty term added in the model. In this work a regression model is presented for a renewal process with Weibull distribution for the times between occurrences. The modeling considers censored times and a frailty variable to explain the relationship among the recurrence times of a unit. The methodology was developed for the situation where several units are submitted by recurrent events. The empirical probabilities of coverage of the asymptotic normal confidence interval and the behavior of the variances of the estimators were analyzed in the simulations performed. The presence of censures in the sample inflated the variances of the maximum likelihood estimators besides to produce biased estimates for the regression parameters. The bias of the estimator was corrected by "bootstrap" procedure. The analysis of the probability of empirical coverage of the asymptotic confidence intervals, without frailty, presented a good approximation to the nominal values, but some observations about procedures have to be made on the symmetry of the empirical distributions. The frailty term incorporated at the modeling disturbed the maximum likelihood estimation increasing estimators' variability, directly associated to the variance of the fragility term. In the most of the cases, the empirical coverages of the asymptotic confidence intervals were overestimated, with satisfactory results just for the shape parameter of the Weibull distribution.
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Hashimoto, Elizabeth Mie. "Modelo de regressão gama-G em análise de sobrevivência." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-26042013-095312/.
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Dados de tempo de falha são caracterizados pela presença de censuras, que são observações que não foram acompanhadas até a ocorrência de um evento de interesse. Para estudar o comportamento de dados com essa natureza, distribuições de probabilidade são utilizadas. Além disso, é comum se ter uma ou mais variáveis explicativas associadas aos tempos de falha. Dessa forma, o objetivo geral do presente trabalho é propor duas novas distribuições utilizando a função geradora de distribuições gama, no contexto de modelos de regressão em análise de sobrevivência. Essa função possui um parâmetro de forma que permite criar famílias paramétricas de distribuições que sejam flexíveis para capturar uma ampla variedade de comportamentos simétricos e assimétricos. Assim, a distribuição Weibull e a distribuição log-logística foram modificadas, dando origem a duas novas distribuições de probabilidade, denominadas de gama-Weibull e gama-log-logística, respectivamente. Consequentemente, os modelos de regressão locação-escala, de longa-duração e com efeito aleatório foram estudados, considerando as novas distribuições de probabilidade. Para cada um dos modelos propostos, foi utilizado o método da máxima verossimilhança para estimar os parâmetros e algumas medidas de diagnóstico de influência global e local foram calculadas para encontrar possíveis pontos influentes. No entanto, os resíduos foram propostos apenas para os modelos locação-escala para dados com censura à direita e para dados com censura intervalar, bem um estudo de simulação para verificar a distribuição empírica dos resíduos. Outra questão explorada é a introdução dos modelos: gama-Weibull inflacionado de zeros e gama-log-logística inflacionado de zeros, para analisar dados de produção de óleo de copaíba. Por fim, diferentes conjunto de dados foram utilizados para ilustrar a aplicação de cada um dos modelos propostos.Failure time data are characterized by the presence of censoring, which are observations that were not followed up until the occurrence of an event of interest. To study the behavior of the data of that nature, probability distributions are used. Furthermore, it is common to have one or more explanatory variables associated to failure times. Thus, the goal of this work is given to the generating of gamma distributions function in the context of regression models in survival analysis. This function has a shape parameter that allows create parametric families of distributions that are flexible to capture a wide variety of symmetrical and asymmetrical behaviors. Therefore, through the generating of gamma distributions function, the Weibull distribution and log-logistic distribution were modified to give two new probability distributions: gamma-Weibull and gammalog-logistic. Additionally, location-scale regression models, long-term models and models with random effects were also studied, considering the new distributions. For each of the proposed models, we used the maximum likelihood method to estimate the parameters and some diagnostic measures of global and local influence were calculated for possible influential points. However, residuals have been proposed for data with right censoring and interval-censored data and a simulation study to verify the empirical distribution of the residuals. Another issue explored is the introduction of models: gamma-Weibull inflated zeros and gamma-log-logistic inflated zeros, to analyze production data copaiba oil. Finally, different data set are used to illustrate the application of each of the models.
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Läuter, Henning. "Estimation in partly parametric additive Cox models." Universität Potsdam, 2003. http://opus.kobv.de/ubp/volltexte/2011/5150/.
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The dependence between survival times and covariates is described e.g. by proportional hazard models. We consider partly parametric Cox models and discuss here the estimation of interesting parameters. We represent the ma- ximum likelihood approach and extend the results of Huang (1999) from linear to nonlinear parameters. Then we investigate the least squares esti- mation and formulate conditions for the a.s. boundedness and consistency of these estimators.
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Lopes, Marina Travassos. "Modelos estatísticos para suporte a avaliação cirúrgica em crianças portadoras de cardiopatias congênitas." Universidade Federal da Paraíba, 2017. http://tede.biblioteca.ufpb.br:8080/handle/tede/9057.
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Heart diseases are responsible for more deaths in the first year of life than any other congenital problem in Brazil, affecting 8 to 10 children per 1000 live births. There are several types of heart diseases, some heal with time others require surgery. Evaluating the characteristics of the surgeries, it is possible to obtain the probability of the occurrence of postoperative complications and the estimation of the length of stay in the ICU (Intensive Care Unit) that varies according to the typology of this occurrence and the patient health condition. In this sense, the use of statistical models can help to optimize the care of patients in unfavorable clinical conditions. The aim of this study is to develop a tool based on statistical models to assist decision making about the chronological order of the surgeries to be performed. The data from this study came from the charts of the children destined to the execution of the surgery of congenital heart disease in the reference center that composes the Pediatric Cardiology Network PE-PB in the State of Paraíba. A logistic regression model was used to estimate the probability of occurrence of postoperative complications and survival analysis techniques to detect differences between the influence of determining factors on the length of ICU stay after the surgery. All data were analyzed in statistical software R, version 3.2.0. A total of 130 children were included, which 86.15% being below 10 years of age and weighing between 5 and 25 kg. Of the 72 children who presented post-surgical complications, 22.3% presented shunt-type cardiopathy, and 10% had Patent Ductus Arteriosus, followed by 9.2% with Tetralogy of Fallot. The risk factors identified by logistic regression as more associated with the outcome "developing post-surgical complications" were: high risk score (OR = 12.9; p-value = 0.02), presence of acyanotic obstructive heart disease (OR = 12.5, p-value = 0.006), the aortic clamping time during surgery greater than 20 minutes (OR = 3.3; p-value = 0.01), the time of extubation during the surgery (OR = 1.1, p-value = 0.07), presence of pulmonary arterial hypertension (OR = 6.7, p-value = 0.09) and age less than 6 months (OR = 3, 6; p-value = 0.05). In the survival analysis, it was possible to verify that there are statistically significant differences in length of ICU stay between children less than 6 months and older children; Also among children who presented high surgical risk and those who did not present; And among children where there is presence or absence of pulmonary arterial hypertension, in which the presence of some of these characteristics implies a greater probability of permanence for a certain time in the ICU. Also through the survival analysis, it was possible to observe that besides the factors identified through the logistic regression, the occurrence of postoperative infection in children also entails a longer hospitalization time after the surgery. Both techniques analyzed together, were able to build estimates for a certain hospital stay in cases of occurrence or not of postoperative complications, bringing support to hospital planning decisions, resulting in the optimization of the rotation of the available beds, in addition to the suggestion of chronological order of the queue of the next surgeries of congenital cardiopathy to be performed.
As cardiopatias são responsáveis por mais mortes no primeiro ano de vida do que qualquer outro problema congênito no Brasil, acometendo de 8 a 10 crianças a cada 1000 nascidos vivos. Existem diversos tipos de cardiopatia, algumas curam com o tempo, outras requerem intervenções cirúrgicas. Avaliando as características das cirurgias, é possível obter a probabilidade da ocorrência de complicações pós-cirúrgicas, e a estimativa do tempo de internamento em UTI que varia de acordo com a tipologia dessa ocorrência e com o perfil clínico do paciente. Neste sentido, a utilização de modelos estatísticos, pode auxiliar a otimização do cuidado a pacientes em condições clínicas desfavoráveis, sendo a proposta deste estudo, desenvolver uma ferramenta baseada em modelos estatísticos para auxiliar à tomada de decisões acerca da ordem cronológica das cirurgias a serem executadas. Os dados desse estudo provieram dos prontuários das crianças destinadas à execução da cirurgia de cardiopatia congênita no centro de referência que compõe a Rede de Cardiologia Pediátrica PE-PB no Estado da Paraíba. O modelo de regressão logística foi utilizado para estimar a probabilidade de ocorrência de complicações pós-cirúrgicas e as técnicas de análise de sobrevivência, para detectar diferenças entre a influência de fatores determinantes sobre os tempos de internamento em Unidades de Terapia Intensiva após a realização das cirurgias. Todos os dados foram analisados no software estatístico R, versão 3.2.0. Foram incluídas 130 crianças, sendo 86,15% com idade inferior a 10 anos de idade e peso se concentrando entre 5 e 25 quilos. Das 72 crianças que apresentaram complicações pós-cirúrgicas, 22,3% apresentaram a cardiopatia do tipo shunt, e no tocante ao diagnóstico, observou-se que 10% eram portadores de Persistência do Canal Arterial, seguido de 9,2% portadores de Tetralogia de Fallot. Os fatores de risco identificados pela regressão logística como mais associados com o desfecho “desenvolver complicações pós-cirúrgicas” foram: apresentar escore de risco alto (OR=12,9; p-valor=0,02), a presença de cardiopatia acianótica obstrutiva (OR=12,5; p-valor=0,006), o tempo de clampeamento aórtico durante a cirurgia ser superior a 20 minutos (OR=3,3; p-valor=0,01), o tempo de extubação durante a realização da cirurgia (OR=1,1; p-valor=0,07), a presença de hipertensão arterial pulmonar (OR=6,7; p-valor=0,09) e idade inferior a 6 meses (OR=3,6; p-valor=0,05). Na análise de sobrevivência, foi possível constatar que existem diferenças estatisticamente significativas sobre o tempo de internamento em UTI entre as crianças com menos de 6 meses de idade e as crianças com idade superior; também entre as crianças que apresentaram alto risco cirúrgico e as que não apresentaram; e entre as crianças onde há presença ou ausência de hipertensão arterial pulmonar, em que a presença de alguma(s) dessas características implica em maiores probabilidades de permanência por um determinado tempo em UTI. Ainda através da análise de sobrevivência, foi possível observar que além dos fatores identificados através da regressão logística, a ocorrência de infecção pós-operatória nas crianças também acarreta maior tempo de internamento após a cirurgia. Ambas as técnicas analisadas conjuntamente, foram capazes de construir estimativas para um determinado tempo de internamento hospitalar em casos de ocorrência ou não de complicações pós-cirúrgicas, trazendo apoio às decisões do planejamento hospitalar, resultando na otimização da rotatividade dos leitos disponíveis, além da sugestão de ordenação cronológica da fila de espera das próximas cirurgias de cardiopatia congênita a serem executadas.