Relevant bibliographies by topics / Proportion hazards model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Proportion hazards model'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Proportion hazards model"

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Calsavara, Vinicius F., Eder A. Milani, Eduardo Bertolli, and Vera Tomazella. "Long-term frailty modeling using a non-proportional hazards model: Application with a melanoma dataset." Statistical Methods in Medical Research 29, no. 8 (November 6, 2019): 2100–2118. http://dx.doi.org/10.1177/0962280219883905.

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The semiparametric Cox regression model is often fitted in the modeling of survival data. One of its main advantages is the ease of interpretation, as long as the hazards rates for two individuals do not vary over time. In practice the proportionality assumption of the hazards may not be true in some situations. In addition, in several survival data is common a proportion of units not susceptible to the event of interest, even if, accompanied by a sufficiently large time, which is so-called immune, “cured,” or not susceptible to the event of interest. In this context, several cure rate models are available to deal with in the long term. Here, we consider the generalized time-dependent logistic (GTDL) model with a power variance function (PVF) frailty term introduced in the hazard function to control for unobservable heterogeneity in patient populations. It allows for non-proportional hazards, as well as survival data with long-term survivors. Parameter estimation was performed using the maximum likelihood method, and Monte Carlo simulation was conducted to evaluate the performance of the models. Its practice relevance is illustrated in a real medical dataset from a population-based study of incident cases of melanoma diagnosed in the state of São Paulo, Brazil.
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Bělasková, Silvie, and Eva Fiserová. "Improvement of the accuracy in testing the effect in the cox proportional hazards model using higher order approximations." Filomat 31, no. 18 (2017): 5591–601. http://dx.doi.org/10.2298/fil1718591b.

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Small-sample properties of the likelihood ratio test, the Wald test and the score test about significance of the effect in the Cox proportional hazards model for the right-censored and left-truncated data are investigated. These are large-sample tests, and, therefore, these are only approximate tests and they do not necessary maintain chosen significance level. Consequently, the p-value can be inaccurate as well. Higher order approximations of the likelihood function based on the Barndorff-Nielsen formula and the Lugannani-Rice formula are used in order to improve the accuracy of statistical inferences. The accuracy of these tests together with proposed approximations are compared by means of simulations under conditions of decreasing the sample size, and increasing proportion of right-censored and left-truncated data in the Cox model with the exponential and the Weibull distribution of the baseline hazard function. The results show that higher order approximations based on the Lugannani-Rice and the Barndorff-Nielsen formulas in combination with the Wald statistic improve the accuracy.
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Li, Jinchao, Lin Chen, Yuwei Xiang, and Ming Xu. "Research on Influential Factors of PM2.5 within the Beijing-Tianjin-Hebei Region in China." Discrete Dynamics in Nature and Society 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/6375391.

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Air pollutant emissions are problematic hazards in China, especially in the Beijing-Tianjin-Hebei region. In this paper, we use fishbone method to set up the influential factor set of PM2.5 qualitatively. Then we use Spearman rank correlation test and panel data regression model to analyze the data of Beijing-Tianjin-Hebei region from 2012 to 2015 quantitatively. The results show that population density, energy consumption per unit area, concrete production per unit area, industrial proportion, transportation volume per unit area, new construction areas per unit area, road construction length per unit area, and coal consumption proportion are all positively correlated with PM2.5. The proportion of electricity consumption is negatively correlated with PM2.5. Among them, population density, industrial proportion, transportation volume, energy consumption per unit area, and the proportion of electricity consumption have a pivotal influence on PM2.5. At last, we give some suggestions to solve the hazard of PM2.5 in Beijing-Tianjin-Hebei region.
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Tang, Haijing, Guo Chen, Yu Kang, and Xu Yang. "Application of Data Science Technology on Research of Circulatory System Disease Prediction Based on a Prospective Cohort." Algorithms 11, no. 10 (October 20, 2018): 162. http://dx.doi.org/10.3390/a11100162.

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Chronic diseases represented by circulatory diseases have gradually become the main types of diseases affecting the health of our population. Establishing a circulatory system disease prediction model to predict the occurrence of diseases and controlling them is of great significance to the health of our population. This article is based on the prospective population cohort data of chronic diseases in China, based on the existing medical cohort studies, the Kaplan–Meier method was used for feature selection, and the traditional medical analysis model represented by the Cox proportional hazards model was used and introduced. Support vector machine research methods in machine learning establish circulatory system disease prediction models. This paper also attempts to introduce the proportion of the explanation variation (PEV) and the shrinkage factor to improve the Cox proportional hazards model; and the use of Particle Swarm Optimization (PSO) algorithm to optimize the parameters of SVM model. Finally, the experimental verification of the above prediction models is carried out. This paper uses the model training time, Accuracy rate(ACC), the area under curve (AUC)of the Receiver Operator Characteristic curve (ROC) and other forecasting indicators. The experimental results show that the PSO-SVM-CSDPC disease prediction model and the S-Cox-CSDPC circulation system disease prediction model have the advantages of fast model solving speed, accurate prediction results and strong generalization ability, which are helpful for the intervention and control of chronic diseases.
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Alipour, Abbas, Abolghasem Shokri, Fatemeh Yasari, and Soheila Khodakarim. "Introduction to Competing Risk Model in the Epidemiological Research." International Journal of Epidemiologic Research 5, no. 3 (September 15, 2018): 98–102. http://dx.doi.org/10.15171/ijer.2018.21.

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Background and aims: Chronic kidney disease (CKD) is a public health challenge worldwide, with adverse consequences of kidney failure, cardiovascular disease (CVD), and premature death. The CKD leads to the end-stage of renal disease (ESRD) if late/not diagnosed. Competing risk modeling is a major issue in epidemiology research. In epidemiological study, sometimes, inappropriate methods (i.e. Kaplan-Meier method) have been used to estimate probabilities for an event of interest in the presence of competing risks. In these situations, competing risk analysis is preferred to other models in survival analysis studies. The purpose of this study was to describe the bias resulting from the use of standard survival analysis to estimate the survival of a patient with ESRD and to provide alternate statistical methods considering the competing risk. Methods: In this retrospective study, 359 patients referred to the hemodialysis department of Shahid Ayatollah Ashrafi Esfahani hospital in Tehran, and underwent continuous hemodialysis for at least three months. Data were collected through patient’s medical history contained in the records (during 2011-2017). To evaluate the effects of research factors on the outcome, cause-specific hazard model and competing risk models were fitted. The data were analyzed using Stata (a general-purpose statistical software package) software, version 14 and SPSS software, version 21, through descriptive and analytical statistics. Results: The median duration of follow-up was 3.12 years and mean age at ESRD diagnosis was 66.47 years old. Each year increase in age was associated with a 98% increase in hazard of death. In this study, statistical analysis based on the competing risk model showed that age, age of diagnosis, level of education (under diploma), and body mass index (BMI) were significantly associated with death (hazard ratio [HR]=0.98, P<0.001, HR=0.99, P<0.001, HR=2.66, P=0.008, and HR=0.98, P<0.020, respectively). Conclusion: In analysis of competing risk data, it was found that providing both the results of the event of interest and those of competing risks were of importance. The Cox model, which ignored the competing risks, presented the different estimates and results as compared to the proportional sub-distribution hazards model. Thus, it was revealed that in the analysis of competing risks data, the sub-distribution proportion hazards model was more appropriate than the Cox model.
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Lee, Seung-Hwan. "Weighted Log-Rank Statistics for Accelerated Failure Time Model." Stats 4, no. 2 (May 3, 2021): 348–58. http://dx.doi.org/10.3390/stats4020023.

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This paper improves the sensitivity of the Gρ family of weighted log-rank tests for the accelerated failure time model, accommodating realistic alternatives in survival analysis with censored data, such as heavy censoring and crossing hazards. The procedures are based on a weight function with the censoring proportion incorporated as a component. Extensive simulations show that the weight function enhances the performance of the Gρ family, increasing its sensitivity and flexibility. The weight function method is illustrated with an example concerning vaginal cancer.
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Meleth, Sreelatha, Chakrapani Chatla, Venkat R. Katkoori, Billie Anderson, James M. Hardin, Nirag C. Jhala, Al Bartolucci, William E. Grizzle, and Upender Manne. "Comparison of Predicted Probabilities of Proportional Hazards Regression and Linear Discriminant Analysis Methods Using a Colorectal Cancer Molecular Biomarker Database." Cancer Informatics 3 (January 2007): 117693510700300. http://dx.doi.org/10.1177/117693510700300018.

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Background Although a majority of studies in cancer biomarker discovery claim to use proportional hazards regression (PHREG) to the study the ability of a biomarker to predict survival, few studies use the predicted probabilities obtained from the model to test the quality of the model. In this paper, we compared the quality of predictions by a PHREG model to that of a linear discriminant analysis (LDA) in both training and test set settings. Methods The PHREG and LDA models were built on a 491 colorectal cancer (CRC) patient dataset comprised of demographic and clinicopathologic variables, and phenotypic expression of p53 and Bcl-2. Two variable selection methods, stepwise discriminant analysis and the backward selection, were used to identify the final models. The endpoint of prediction in these models was five-year post-surgery survival. We also used linear regression model to examine the effect of bin size in the training set on the accuracy of prediction in the test set. Results The two variable selection techniques resulted in different models when stage was included in the list of variables available for selection. However, the proportion of survivors and non-survivors correctly identified was identical in both of these models. When stage was excluded from the variable list, the error rate for the LDA model was 42% as compared to an error rate of 34% for the PHREG model. Conclusions This study suggests that a PHREG model can perform as well or better than a traditional classifier such as LDA to classify patients into prognostic classes. Also, this study suggests that in the absence of the tumor stage as a variable, Bcl-2 expression is a strong prognostic molecular marker of CRC.
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Tice, Jeffrey A., Diana L. Miglioretti, Chin-Shang Li, Celine M. Vachon, Charlotte C. Gard, and Karla Kerlikowske. "Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer." Journal of Clinical Oncology 33, no. 28 (October 1, 2015): 3137–43. http://dx.doi.org/10.1200/jco.2015.60.8869.

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Purpose Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density. Methods We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC. Results We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P < .001). Conclusion The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model.
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Nishimura, Jennifer, Audrey Choi, Sharon Kim, and Julian Kim. "Trends in the use of breast-conserving surgery and adjuvant radiation therapy in patients with DCIS: A U.S. population-based analysis from 1996 to 2007." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 1110. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.1110.

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1031 Background: The treatment for patients with DCIS remains controversial. Current guidelines based upon best available evidence suggest that breast conserving surgery (BCS) followed by adjuvant radiation therapy (RT) result in acceptable local control and breast cancer specific survival. The purpose of this study was to analyze trends in patterns of care as well as identify factors associated with surgery type and use of adjuvant radiation therapy in a select cohort of patients enrolled into the SEER database. Methods: The study included females 18 years and older with focal DCIS and known tumor size of 5 cm or less diagnosed between 1996 and 2007. The Cochran-Armitage trend test was applied to identify trends in the use of BCS and RT over time. Multivariate logistic regression analyses were used to determine factors associated with receiving BCS vs. mastectomy and BCS plus RT vs. BCS alone. Cox proportional hazard model was used to determine associations with breast cancer-specific mortality. Results: Of the 34,233 women with DCIS, 76.59% were treated with BCS. 66.36% of BCS patients received adjuvant RT over the study period. The proportion of women receiving BCS increased from 71.5% in 1996 to 76.9% in 2007 (p<0.0001). Additionally, the proportion of women who underwent BCS and received adjuvant radiation therapy over the same time period increased from 55.3% to 69.7% (p<0.0001). Multivariate analysis demonstrated that year of diagnosis, race, marital status, geographic region, tumor size, tumor grade and comedo necrosis all were significantly associated with the use of adjuvant radiation therapy, but age was not. Cox proportional hazards models did not associate either surgery type or use of adjuvant radiation in patients undergoing BCS with breast cancer-specific mortality. Conclusions: Based upon reporting within the SEER database, the proportion of DCIS patients undergoing BCS and the BCS patients receiving adjuvant radiation increased over the study time period. Surgery type and use of adjuvant radiation therapy in patients with BCS was not associated with decreased risk of breast-cancer specific death in this cohort.
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GHODRATI, BEHZAD, ALIREZA AHMADI, and DIEGO GALAR. "SPARE PARTS ESTIMATION FOR MACHINE AVAILABILITY IMPROVEMENT ADDRESSING ITS RELIABILITY AND OPERATING ENVIRONMENT — CASE STUDY." International Journal of Reliability, Quality and Safety Engineering 20, no. 03 (June 2013): 1340005. http://dx.doi.org/10.1142/s0218539313400056.

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Industrial operation cost analysis shows that, in general, maintenance represents a significant proportion of the overall operating costs. Therefore, the improvement of maintenance follows the final goal of any company, namely, to maximize profit. This paper studies spare parts availability, an issue of the maintenance process, which is an important way to improve production through increased availability of functional machinery and subsequent minimization of the total production cost. Spare parts estimation based on machine reliability characteristics and operating environment is performed. The study uses an improved statistical-reliability (S-R) approach which incorporates the system/machine operating environment information in systems reliability analysis. For this purpose, two methods of Poisson process and renewal process are introduced and discussed. The renewal process model uses a multiple regression type of analysis based on Cox's proportional hazards modeling (PHM). The parametric approaches with baseline Weibull hazard functions and time-independent covariates are considered, and the influence of operating environment factors on this model is analyzed. The outputs represent a significant difference in the required spare parts estimation when considering or ignoring the influence of the relevant system operating environment. The difference is significant in the sense of spare parts forecasting and inventory management which can enhance the parts and consequently machine availability, leading to economical operation and savings.
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Dissertations / Theses on the topic "Proportion hazards model"

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Crumer, Angela Maria. "Comparison between Weibull and Cox proportional hazards models." Kansas State University, 2011. http://hdl.handle.net/2097/8787.

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Master of Science
Department of Statistics
James J. Higgins
The time for an event to take place in an individual is called a survival time. Examples include the time that an individual survives after being diagnosed with a terminal illness or the time that an electronic component functions before failing. A popular parametric model for this type of data is the Weibull model, which is a flexible model that allows for the inclusion of covariates of the survival times. If distributional assumptions are not met or cannot be verified, researchers may turn to the semi-parametric Cox proportional hazards model. This model also allows for the inclusion of covariates of survival times but with less restrictive assumptions. This report compares estimates of the slope of the covariate in the proportional hazards model using the parametric Weibull model and the semi-parametric Cox proportional hazards model to estimate the slope. Properties of these models are discussed in Chapter 1. Numerical examples and a comparison of the mean square errors of the estimates of the slope of the covariate for various sample sizes and for uncensored and censored data are discussed in Chapter 2. When the shape parameter is known, the Weibull model far out performs the Cox proportional hazards model, but when the shape parameter is unknown, the Cox proportional hazards model and the Weibull model give comparable results.
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Meier, Amalia Sophia. "Discrete proportional hazards models for uncertain outcomes /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/9579.

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Betton, Sandra Ann. "Bankruptcy : a proportional hazard approach." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26056.

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The recent dramatic increase in the corporate bankruptcy rate, coupled with a similar rate of increase in the bank failure rate, has re-awakened investor, lender and government interest in the area of bankruptcy prediction. Bankruptcy prediction models are of particular value to a firm's current and future creditors who often do not have the benefit of an actively traded market in the firm's securities from which to make inferences about the debtor's viability. The models commonly used by many experts in an endeavour to predict the possibility of disaster are outlined in this paper. The proportional hazard model, pioneered by Cox [1972], assumes that the hazard function, the risk of failure, given failure has not already occurred, is a function of various explanatory variables and estimated coefficients multiplied by an arbitrary and unknown function of time. The Cox Proportional Hazard model is usually applied in medical studies; but, has recently been applied to the bank failure question [Lane, Looney & Wansley, 1986]. The model performed well in the narrowly defined, highly regulated, banking industry. The principal advantage of this approach is that the model incorporates both the survival times observed and any censoring of data thereby using more of the available information in the analysis. Unlike many bankruptcy prediction models, such as logit and probit based regression models, the Cox model estimates the probability distribution of survival times. The proportional hazard model would, therefore, appear to offer a useful addition to the more traditional bankruptcy prediction models mentioned above. This paper evaluates the applicability of the Cox proportional hazard model in the more diverse industrial environment. In order to test this model, a sample of 109 firms was selected from the Compustat Industrial and Research Industrial data tapes. Forty one of these firms filed petitions under the various bankruptcy acts applicable between 1972 and 1985 and were matched to 67 firms which had not filed petitions for bankruptcy during the same period. In view of the dramatic changes in the bankruptcy regulatory environment caused by the Bankruptcy reform act of 1978, the legal framework of the bankruptcy process was also examined. The performance of the estimated Cox model was then evaluated by comparing its classification and descriptive capabilities to those of an estimated discriminant analysis based model. The results of this study indicate that while the classification capability of the Cox model was less than that of discriminant analysis, the model provides additional information beyond that available from the discriminant analysis.
Business, Sauder School of
Graduate
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Ishak, Khajak. "Omitting a strong covariate from Cox's proportional hazards model." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33005.

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The primary objective of this thesis is to explore the effect of omitting a strong prognostic factor from Cox's proportional hazards model when analyzing data from randomized trials. The secondary objective is to provide an overview of the Cox model. We first present the properties of the model, the method of maximum partial likelihood and the elements available on which to draw inferences concerning parameters. We then discuss various methods used to assess the tenability of the proportional hazards assumption as well as ways to incorporate non-proportional hazards in the model.
In the third and final chapter, we address the primary objective of the thesis. In linear regression analysis, unbiased estimates of the effect of the intervention can be obtained even when important but balanced determinants of the outcome are omitted from the model; the precision of the estimates are improved, however, with the inclusion of strong covariates. The logistic and Cox regression (and other non-linear) models do not share this property, however. We discuss the literature on this topic and provide examples to illustrate the problem. We examine the situation for the Cox model in more detail with the analysis of data from an experiment on the effect of increased sexual activity on the longevity of male fruitflies.
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Mair, Patrick, and Marcus Hudec. "Session Clustering Using Mixtures of Proportional Hazards Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/598/1/document.pdf.

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Emanating from classical Weibull mixture models we propose a framework for clustering survival data with various proportionality restrictions imposed. By introducing mixtures of Weibull proportional hazards models on a multivariate data set a parametric cluster approach based on the EM-algorithm is carried out. The problem of non-response in the data is considered. The application example is a real life data set stemming from the analysis of a world-wide operating eCommerce application. Sessions are clustered due to the dwell times a user spends on certain page-areas. The solution allows for the interpretation of the navigation behavior in terms of survival and hazard functions. A software implementation by means of an R package is provided. (author´s abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Sarker, Md Shah Jalal. "Tests for Weibull based proportional hazards frailty models." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/1046/.

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Oman, J. P. "Case influence in proportional hazards with an application in renal transplantation." Thesis, University of Newcastle Upon Tyne, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287419.

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He, Bin. "APPLICATION OF THE EMPIRICAL LIKELIHOOD METHOD IN PROPORTIONAL HAZARDS MODEL." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4384.

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In survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of the model assumptions is a key component. However, the presence of complicated types of censoring such as double censoring and partly interval-censoring in survival data makes model assessment difficult, and the existing tests for goodness-of-fit do not have direct extension to these complicated types of censored data. In this work, we use empirical likelihood (Owen, 1988) approach to construct goodness-of-fit test and provide estimates for the Cox model with various types of censored data. Specifically, the problems under consideration are the two-sample Cox model and stratified Cox model with right censored data, doubly censored data and partly interval-censored data. Related computational issues are discussed, and some simulation results are presented. The procedures developed in the work are applied to several real data sets with some discussion.
Ph.D.
Department of Mathematics
Sciences
Mathematics
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Li, Qiuju. "Statistical inference for joint modelling of longitudinal and survival data." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/statistical-inference-for-joint-modelling-of-longitudinal-and-survival-data(65e644f3-d26f-47c0-bbe1-a51d01ddc1b9).html.

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In longitudinal studies, data collected within a subject or cluster are somewhat correlated by their very nature and special cares are needed to account for such correlation in the analysis of data. Under the framework of longitudinal studies, three topics are being discussed in this thesis. In chapter 2, the joint modelling of multivariate longitudinal process consisting of different types of outcomes are discussed. In the large cohort study of UK north Stafforshire osteoarthritis project, longitudinal trivariate outcomes of continuous, binary and ordinary data are observed at baseline, year 3 and year 6. Instead of analysing each process separately, joint modelling is proposed for the trivariate outcomes to account for the inherent association by introducing random effects and the covariance matrix G. The influence of covariance matrix G on statistical inference of fixed-effects parameters has been investigated within the Bayesian framework. The study shows that by joint modelling the multivariate longitudinal process, it can reduce the bias and provide with more reliable results than it does by modelling each process separately. Together with the longitudinal measurements taken intermittently, a counting process of events in time is often being observed as well during a longitudinal study. It is of interest to investigate the relationship between time to event and longitudinal process, on the other hand, measurements taken for the longitudinal process may be potentially truncated by the terminated events, such as death. Thus, it may be crucial to jointly model the survival and longitudinal data. It is popular to propose linear mixed-effects models for the longitudinal process of continuous outcomes and Cox regression model for survival data to characterize the relationship between time to event and longitudinal process, and some standard assumptions have been made. In chapter 3, we try to investigate the influence on statistical inference for survival data when the assumption of mutual independence on random error of linear mixed-effects models of longitudinal process has been violated. And the study is conducted by utilising conditional score estimation approach, which provides with robust estimators and shares computational advantage. Generalised sufficient statistic of random effects is proposed to account for the correlation remaining among the random error, which is characterized by the data-driven method of modified Cholesky decomposition. The simulation study shows that, by doing so, it can provide with nearly unbiased estimation and efficient statistical inference as well. In chapter 4, it is trying to account for both the current and past information of longitudinal process into the survival models of joint modelling. In the last 15 to 20 years, it has been popular or even standard to assume that longitudinal process affects the counting process of events in time only through the current value, which, however, is not necessary to be true all the time, as recognised by the investigators in more recent studies. An integral over the trajectory of longitudinal process, along with a weighted curve, is proposed to account for both the current and past information to improve inference and reduce the under estimation of effects of longitudinal process on the risk hazards. A plausible approach of statistical inference for the proposed models has been proposed in the chapter, along with real data analysis and simulation study.
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Putcha, Venkata Rama Prasad. "Random effects in survival analysis." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312431.

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Books on the topic "Proportion hazards model"

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Oberc, Margaret. Tree structured methods for the proportional hazards model. Toronto: University of Toronto, 1993.

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LeBlanc, Michael R. Step-function covariate effects in the proportional hazards model. Toronto, Ont: University of Toronto, Department of Statistics, 1993.

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Tapia-Aguilar, Alberto. Accurate confidence intervals for regression parameters in proportional hazards model. Toronto: [s.n.], 1994.

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Hastie, Trevor. Exploring the nature of covariate effects in the proportional hazards model. Toronto: University of Toronto, Dept. of statistics, 1988.

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Hein, Putter, ed. Dynamic prediction in clinical survival analysis. Boca Raton: CRC Press, 2012.

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Frijters, Paul. Socio-economic status, health shocks, life satisfaction and mortality: Evidence from an increasing mixed proportional hazard model. Bonn, Germany: IZA, 2005.

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Fallick, Bruce. The recall and new job search of laid-off workers: A bivariate proportional hazard model with unobserved heterogeneity. Washington, D.C: Federal Reserve Board, 2003.

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Vance, Colin. Cities and satellites: Spatial effects and unobserved heterogeneity in the modeling of urban growth. Essen: RWI, 2008.

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Bun, Cheung Yin, Parmar Mahesh K. B, and Parmar Mahesh K. B, eds. Survival analysis: A practical approach. 2nd ed. Chichester, West Sussex, England: Wiley, 2006.

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Watson, Peter. Survival analysis. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0018.

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This chapter explores survival analysis. It includes data censoring, functions of duration time (the survival function, and hazard function), Cox’s proportional hazards model, log-linearity, time varying predictors, and odds ratios.
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Book chapters on the topic "Proportion hazards model"

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Beyersmann, Jan, Martin Schumacher, and Arthur Allignol. "Proportional hazards models." In Competing Risks and Multistate Models with R, 89–153. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-2035-4_5.

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Hausman, Jerry A., and Tiemen M. Woutersen. "Proportional Hazard Model." In The New Palgrave Dictionary of Economics, 1–5. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2625-1.

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Hausman, Jerry A., and Tiemen M. Woutersen. "Proportional Hazard Model." In The New Palgrave Dictionary of Economics, 10901–5. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2625.

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Hausman, Jerry A., and Tiemen M. Woutersen. "Proportional Hazard Model." In Microeconometrics, 197–201. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280816_24.

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Schmidt, Peter, and Ann Dryden Witte. "The Proportional Hazards Model." In Research in Criminology, 83–90. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3772-3_6.

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Beyersmann, Jan, Martin Schumacher, and Arthur Allignol. "Proportional transition hazards models." In Competing Risks and Multistate Models with R, 197–209. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-2035-4_10.

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Wang, Wei, and Chengcheng Hu. "Proportional Hazards Regression Models." In Springer Handbook of Engineering Statistics, 387–96. London: Springer London, 2006. http://dx.doi.org/10.1007/978-1-84628-288-1_21.

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Arnold, Barry C., and Yong Hee Kim. "Conditional Proportional Hazards Models." In Lifetime Data: Models in Reliability and Survival Analysis, 21–28. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-5654-8_4.

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O’Quigley, John. "Non-proportional hazards models." In Survival Analysis, 119–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-33439-0_6.

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Nikulin, Mikhail, and Hong-Dar Isaac Wu. "The Cox Proportional Hazards Model." In The Cox Model and Its Applications, 35–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49332-8_3.

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Conference papers on the topic "Proportion hazards model"

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Lloyd, George M., Timothy Hasselman, and Thomas Paez. "A Proportional Hazards Neural Network for Performing Reliability Estimates and Risk Prognostics for Mobile Systems Subject to Stochastic Covariates." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82657.

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We present a proportional hazards model (PHM) that establishes a framework suitable for performing reliability estimates and risk prognostics on complex multi-component systems which are transferred at arbitrary times among a discrete set of non-stationary stochastic environments. Such a scenario is not at all uncommon for portable and mobile systems. It is assumed that survival data, possibly interval censored, is available at several “typical” environments. This collection of empirical survival data forms the foundation upon which the basic effects of selected covariates are incorporated via the proportional hazards model. Proportional hazards models are well known in medical statistics, and can provide a variety of data-driven risk models which effectively capture the effects of the covariates. The paper describes three modifications we have found most suitable for this class of systems: development of suitable survival estimators that function well under realistic censoring scenarios, our modifications to the PHM which accommodate time-varying stochastic covariates, and implementation of said model in a non-linear network context which is itself model-free. Our baseline hazard is a parameterized reliability model developed from the empirical reliability estimates. Development of the risk score for arbitrary covariates arising from movement among different random environments is through interaction of the non-linear network and training data obtained from a Markov chain simulation based on stochastic environmental responses generated from Karhunen-Loe`ve models.
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Huang, Tingting, and Tongmin Jiang. "An extended proportional hazards-proportional odds model in accelerated life testing." In 2009 8th International Conference on Reliability, Maintainability and Safety (ICRMS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icrms.2009.5270069.

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Chen, He-tao, and Hong-jie Yuan. "Reliability assessment based on proportional degradation hazards model." In EM2010). IEEE, 2010. http://dx.doi.org/10.1109/icieem.2010.5646465.

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"FAILURE PREDICTION USING THE COX PROPORTIONAL HAZARD MODEL." In 6th International Conference on Software and Data Technologies. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003557802010206.

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Mohammad, R., A. Kalam, and S. V. Amari. "Reliability of load-sharing systems subject to proportional hazards model." In 2013 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2013. http://dx.doi.org/10.1109/rams.2013.6517708.

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Guo, Chiming, Yongsheng Bai, Handong Wang, and Rui Tong. "Condition-based Maintenance Optimization for Gearbox Using Proportional Hazards Model." In 2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai). IEEE, 2020. http://dx.doi.org/10.1109/phm-shanghai49105.2020.9280975.

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LU, XUEWEN, and R. S. SINGH. "ONE-STEP ESTIMATION FOR THE PARTIALLY LINEAR PROPORTIONAL HAZARDS MODEL." In Proceedings of Statistics 2001 Canada: The 4th Conference in Applied Statistics. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2002. http://dx.doi.org/10.1142/9781860949531_0014.

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ZHOU, MAI. "THE COX PROPORTIONAL HAZARDS MODEL WITH A PARTIALLY KNOWN BASELINE." In Random Walk, Sequential Analysis and Related Topics - A Festschrift in Honor of Yuan-Shih Chow. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772558_0014.

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Guo, C., J. B. Ren, and Z. Y. Xing. "Reliability model for metro door using the proportional hazard model." In International Conference on Advanced Control, Automation and Robotoics. Southampton, UK: WIT Press, 2015. http://dx.doi.org/10.2495/acar140571.

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Mendes, A. C., and N. Fard. "Reliability modeling for appliances using the Proportional Hazard Model." In 2013 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2013. http://dx.doi.org/10.1109/rams.2013.6517666.

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Reports on the topic "Proportion hazards model"

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Mirel, Lisa. NHSR 155: Comparative Analysis of the National Health and Nutrition Examination Survey Public-Use and Restricted-Use Linked Mortality Files - Production Schedule. National Center for Health Statistics (U.S.), May 2021. http://dx.doi.org/10.15620/cdc:104774.

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This report describes a comparative analysis of the public-use and restricted-use NHANES LMFs. Cox proportional hazards models were used to estimate relative hazard ratios for a standard set of sociodemographic covariates for all-cause as well as cause-specific mortality, using the public-use and restricted-use NHANES LMFs.
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Gelfand, Alan E., and Bani K. Mallick. Bayesian Analysis of Semiparametric Proportional Hazards Models. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada279394.

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McKeague, Ian W., and Klaus J. Utikal. Goodness-of-Fit Tests for Additive Hazards and Proportional Hazards Models. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada202440.

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Lenart, Adam, and Trifon I. Missov. Linking period and cohort life expectancy in Gompertz proportional hazards models. Rostock: Max Planck Institute for Demographic Research, April 2010. http://dx.doi.org/10.4054/mpidr-wp-2010-024.

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Mirel, Lisa, Cindy Zhang, Christine Cox, Ye Yeats, Félix Suad El Burai, and Golden Cordell. Comparative analysis of the National Health and Nutrition Examination Survey public-use and restricted-use linked mortality files. Centers for Disease Control and Prevention (U.S.), May 2021. http://dx.doi.org/10.15620/cdc:104744.

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"Objectives—Linking national survey data with administrative data sources enables researchers to conduct analyses that would not be possible with each data source alone. Recently, the Data Linkage Program at the National Center for Health Statistics (NCHS) released updated Linked Mortality Files, including the National Health and Nutrition Examination Survey data linked to the National Death Index mortality files. Two versions of the files were released: restricted-use files available through NCHS and Federal Statistical Research Data Centers and public-use files. To reduce the reidentification risk, statistical disclosure limitation methods were applied to the public-use files before they were released. This included limiting the amount of mortality information available and perturbing cause of death and follow-up time for select records. Methods—To assess the comparability of the restricted-use and public-use files, relative hazard ratios for all-cause and cause-specific mortality using Cox proportional hazards models were estimated and compared. Results—The comparative analysis found that the two data files yield similar descriptive and model results. Suggested citation: Mirel LB, Zhang C, Cox CS, Ye Y, El Burai Félix S, Golden C. Comparative analysis of the National Health and Nutrition Examination Survey public-use and restricted-use linked mortality files. National Health Statistics Reports; no 155. Hyattsville, MD: National Center for Health Statistics. 2021. DOI: https://doi.org/10.15620/cdc:104744. CS323656 nhsr155-508.pdf"
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