Relevant bibliographies by topics / Proportional hazards(PH) model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Proportional hazards(PH) model'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Proportional hazards(PH) model"

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Farooq, Fabiha Binte, and Md Jamil Hasan Karami. "Model Selection Strategy for Cox Proportional Hazards Model." Dhaka University Journal of Science 67, no. 2 (July 30, 2019): 111–16. http://dx.doi.org/10.3329/dujs.v67i2.54582.

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Often in survival regression modelling, not all predictors are relevant to the outcome variable. Discarding such irrelevant variables is very crucial in model selection. In this research, under Cox Proportional Hazards (PH) model we study different model selection criteria including Stepwise selection, Least Absolute Shrinkage and Selection Operator (LASSO), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and the extended versions of AIC and BIC to the Cox model. The simulation study shows that varying censoring proportions and correlation coefficients among the covariates have great impact on the performances of the criteria to identify a true model. In the presence of high correlation among the covariates, the success rate for identifying the true model is higher for LASSO compared to other criteria. The extended version of BIC always shows better result than the traditional BIC. We have also applied these techniques to real world data. Dhaka Univ. J. Sci. 67(2): 111-116, 2019 (July)
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Asadi, Majid, Nader Ebrahimi, and Ehsan S. Soofi. "Connections of Gini, Fisher, and Shannon by Bayes risk under proportional hazards." Journal of Applied Probability 54, no. 4 (November 30, 2017): 1027–50. http://dx.doi.org/10.1017/jpr.2017.51.

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Abstract The proportional hazards (PH) model and its associated distributions provide suitable media for exploring connections between the Gini coefficient, Fisher information, and Shannon entropy. The connecting threads are Bayes risks of the mean excess of a random variable with the PH distribution and Bayes risks of the Fisher information of the equilibrium distribution of the PH model. Under various priors, these Bayes risks are generalized entropy functionals of the survival functions of the baseline and PH models and the expected asymptotic age of the renewal process with the PH renewal time distribution. Bounds for a Bayes risk of the mean excess and the Gini's coefficient are given. The Shannon entropy integral of the equilibrium distribution of the PH model is represented in derivative forms. Several examples illustrate implementation of the results and provide insights for potential applications.
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ZHANG, HAO, and ELSAYED A. ELSAYED. "NONPARAMETRIC ACCELERATED LIFE TESTING BASED ON PROPORTIONAL ODDS MODEL." International Journal of Reliability, Quality and Safety Engineering 13, no. 04 (August 2006): 365–78. http://dx.doi.org/10.1142/s0218539306002318.

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Accelerated life testing (ALT) is used to obtain failure time data in short duration under high stress levels in order to predict product life and performance under design conditions. The proportional hazards (PH) model, a widely used reliability prediction model, assumes constant ratio between the failure rate at high stress levels and the failure rate at the normal operating conditions. However, this assumption might be violated under some conditions and the prediction of the failure rate at normal conditions becomes inaccurate. We investigate the proportional odds (PO) model, which assumes that the odds ratio under different stress levels is constant, for accelerating life testing. In this research, we propose a nonparametric ALT approach based on the proportional odds model to predict reliability at normal operating conditions. We estimate the parameters of the proposed ALT model using the maximum likelihood estimation method. To verify the new approach, we fit the PO model with simulated failure time datasets and experimental failure data and compare its performance with the PH model. The results show that the new approach based on the PO model is a viable complement to the PH model in estimating reliability of products possessing property of converging hazard rate functions.
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Equeter, Lucas, François Ducobu, Edouard Rivière-Lorphèvre, Roger Serra, and Pierre Dehombreux. "An Analytic Approach to the Cox Proportional Hazards Model for Estimating the Lifespan of Cutting Tools." Journal of Manufacturing and Materials Processing 4, no. 1 (March 24, 2020): 27. http://dx.doi.org/10.3390/jmmp4010027.

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The machining industry raises an ever-growing concern for the significant cost of cutting tools in the production process of mechanical parts, with a focus on the replacement policy of these inserts. While an early maintenance induces lower tool return on investment, scraps and inherent costs stem from late replacement. The framework of this paper is the attempt to predict the tool inserts Mean Up Time, based solely on the value of a cutting parameter (the cutting speed in this particular turning application). More specifically, the use of the Cox Proportional Hazards (PH) Model for this prediction is demonstrated. The main contribution of this paper is the analytic approach that was conducted about the relevance on data transformation prior to using the Cox PH Model. It is shown that the logarithm of the cutting speed is analytically much more relevant in the prediction of the Mean Up Time through the Cox PH model than the raw cutting speed value. The paper also covers a numerical validation designed to show and discuss the benefits of this data transformation and the overall interest of the Cox PH model for the lifetime prognosis. This methodology, however, necessitates the knowledge of an analytical law linking the covariate to the Mean Up Time. It also shows how the necessary data for the numerical experiment was obtained through a gamma process simulating the degradation of cutting inserts. The results of this paper are expected to help manufacturers in the assessment of tool lifespan.
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Tebbi, O., F. Guérin, and B. Dumon. "Standard Accelerated Life Testing Model Applied to Mechanical Components." Journal of the IEST 48, no. 1 (September 1, 2005): 103–14. http://dx.doi.org/10.17764/jiet.48.1.b0640u145jw81346.

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This paper provides an overview of the application of accelerated life testing (ALT) models to mechanical components. Estimates are based upon a classical test plan using a sample system tested under accelerated conditions (not under operating conditions). The time transfer regression model is considered log-linear. The parametric model, proportional hazards (PH) model, and semiparametric model are studied. This paper illustrates an experimental example on a paper clip.
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Sharma, Reema, Richa Srivastava, and Satyanshu K. Upadhyay. "A Hierarchical Bayes Analysis and Comparison of PH Weibull and PH Exponential Models for One-Shot Device Testing Experiment." International Journal of Reliability, Quality and Safety Engineering 28, no. 05 (July 30, 2021): 2150036. http://dx.doi.org/10.1142/s0218539321500364.

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The one-shot devices are highly reliable and, therefore, accelerated life tests are often employed to perform the experiments on such devices. Obviously, in the process, some covariates are introduced. This paper considers the proportional hazards model to observe the effect of covariates on the failure rates under the assumption of two commonly used models, namely the exponential and the Weibull for the lifetimes. The Bayes implementation is proposed using the hybridization of Gibbs and Metropolis algorithms that routinely extend to missing data situations as well. The entertained models are compared using the Bayesian and deviance information criteria and the expected posterior predictive loss criterion. Finally, the results based on two real data examples are given as an illustration.
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Spirko-Burns, Lauren, and Karthik Devarajan. "Unified methods for feature selection in large-scale genomic studies with censored survival outcomes." Bioinformatics 36, no. 11 (March 10, 2020): 3409–17. http://dx.doi.org/10.1093/bioinformatics/btaa161.

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Abstract Motivation One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the disease process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous datasets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards (PH), which is unlikely to hold for each feature. When applied to genomic features exhibiting some form of non-proportional hazards (NPH), these methods could lead to an under- or over-estimation of the effects. We propose a broad array of marginal screening techniques that aid in feature ranking and selection by accommodating various forms of NPH. First, we develop an approach based on Kullback–Leibler information divergence and the Yang–Prentice model that includes methods for the PH and proportional odds (PO) models as special cases. Next, we propose R2 measures for the PH and PO models that can be interpreted in terms of explained randomness. Lastly, we propose a generalized pseudo-R2 index that includes PH, PO, crossing hazards and crossing odds models as special cases and can be interpreted as the percentage of separability between subjects experiencing the event and not experiencing the event according to feature measurements. Results We evaluate the performance of our measures using extensive simulation studies and publicly available datasets in cancer genomics. We demonstrate that the proposed methods successfully address the issue of NPH in genomic feature selection and outperform existing methods. Availability and implementation R code for the proposed methods is available at github.com/lburns27/Feature-Selection. Contact karthik.devarajan@fccc.edu Supplementary information Supplementary data are available at Bioinformatics online.
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Apeagee, Bemgba, P. O. Agada, D. A. Dzaar, and A. A. Ede. "APPLICATION OF COX PROPORTIONAL HAZARDS MODEL IN TIME TO EVENT ANALYSIS OF HIV/AIDS PATIENTS." FUDMA JOURNAL OF SCIENCES 4, no. 3 (September 12, 2020): 185–91. http://dx.doi.org/10.33003/fjs-2020-0403-360.

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The Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS) remains a public health crisis that has contributed to the majority of deaths recorded in the past decade, affecting Nigeria and other countries of the world as it has become drug resistance in some patients. This study was aimed at estimating the effects of covariates on the survival time for HIV/AIDS patients using the Cox PH model. The KM results indicated that 91 patients were males, out of which 31 experienced the event of interest, and 60 (68.9%) were censored, 209 were females, 65 died due to AIDS, and 144 were censored (68.9%) respectively. The results of the Cox PHM indicated that sex, age, and health of patients are positively associated with death due to AIDS with the associated negative length of survival for HIV/AIDS patients with HR (1.149, 1.235, 1.887, and 1.306) respectively. The study concluded that CD4 cell counts are the only variable or covariate that showed a lower risk of death due to AIDS. The results further stated that patients with high CD4 cell counts have lower risks of death due to AIDS but an increase in survival time considering other factors. The study, therefore recommends that survival analysis should be used to assess the various risk factors and the confounding effects associated with them stressing that a patient’s lifestyle should be improved to live healthy as they continue to age older.
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Ren, Yi, Chung-Chou H. Chang, Gabriel L. Zenarosa, Heather E. Tomko, Drew Michael S. Donnell, Hyung-joo Kang, Mark S. Roberts, and Cindy L. Bryce. "Gray’s Time-Varying Coefficients Model for Posttransplant Survival of Pediatric Liver Transplant Recipients with a Diagnosis of Cancer." Computational and Mathematical Methods in Medicine 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/719389.

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Transplantation is often the only viable treatment for pediatric patients with end-stage liver disease. Making well-informed decisions on when to proceed with transplantation requires accurate predictors of transplant survival. The standard Cox proportional hazards (PH) model assumes that covariate effects are time-invariant on right-censored failure time; however, this assumption may not always hold. Gray’s piecewise constant time-varying coefficients (PC-TVC) model offers greater flexibility to capture the temporal changes of covariate effects without losing the mathematical simplicity of Cox PH model. In the present work, we examined the Cox PH and Gray PC-TVC models on the posttransplant survival analysis of 288 pediatric liver transplant patients diagnosed with cancer. We obtained potential predictors through univariable(P<0.15)and multivariable models with forward selection(P<0.05)for the Cox PH and Gray PC-TVC models, which coincide. While the Cox PH model provided reasonable average results in estimating covariate effects on posttransplant survival, the Gray model using piecewise constant penalized splines showed more details of how those effects change over time.
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Tsai, Bor-Wen, John T. Harvey, and Carl L. Monismith. "Application of Weibull Theory in Prediction of Asphalt Concrete Fatigue Performance." Transportation Research Record: Journal of the Transportation Research Board 1832, no. 1 (January 2003): 121–30. http://dx.doi.org/10.3141/1832-15.

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The objectives are to present the feasibility of utilizing the Weibull proportional hazards (PH) model and the Weibull accelerated failure time model of survival analysis to predict in situ pavement fatigue performance from laboratory fatigue test results. A set of WesTrack temperature sensitivity fatigue tests is used as an example to demonstrate how the Weibull PH model works. An example utilizing the deflection data from a heavy vehicle simulator test is given to verify the feasibility of the failure time model. The relationship between mode factor and controlled-deformation fatigue test is discussed using the same example. The Weibull theory approach has potential for use in recursive mechanistic-empirical design procedures.
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Dissertations / Theses on the topic "Proportional hazards(PH) model"

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Fei, Mingwei. "A study of the robustness of Cox's proportional hazards model used in testing for covariate effects." Kansas State University, 2012. http://hdl.handle.net/2097/13528.

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Master of Arts
Department of Statistics
Paul Nelson
There are two important statistical models for multivariate survival analysis, proportional hazards(PH) models and accelerated failure time(AFT) model. PH analysis is most commonly used multivariate approach for analysing survival time data. For example, in clinical investigations where several (known) quantities or covariates, potentially affect patient prognosis, it is often desirable to investigate one factor effect adjust for the impact of others. This report offered a solution to choose appropriate model in testing covariate effects under different situations. In real life, we are very likely to just have limited sample size and censoring rates(people dropping off), which cause difficulty in statistical analysis. In this report, each dataset is randomly repeated 1000 times from three different distributions (Weibull, Lognormal and Loglogistc) with combination of sample sizes and censoring rates. Then both models are evaluated by hypothesis testing of covariate effect using the simulated data using the derived statistics, power, type I error rate and covergence rate for each situation. We would recommend PH method when sample size is small(n<20) and censoring rate is high(p>0.8). In this case, both PH and AFT analyses may not be suitable for hypothesis testing, but PH analysis is more robust and consistent than AFT analysis. And when sample size is 20 or above and censoring rate is 0.8 or below, AFT analysis will have slight higher convergence rate and power than PH, but not much improvement in Type I error rates when sample size is big(n>50) and censoring rate is low(p<0.3). Considering the privilege of not requiring knowledge of distribution for PH analysis, we concluded that PH analysis is robust in hypothesis testing for covariate effects using data generated from an AFT model.
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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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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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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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Pelagia, Ioanna. "Variable selection of fixed effects and frailties for Cox Proportional Hazard frailty models and competing risks frailty models." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/variable-selection-of-fixed-effects-and-frailties-for-cox-proportional-hazard-frailty-models-and-competing-risks-frailty-models(c75c6314-f43e-4d69-a2de-942bece6a404).html.

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This thesis focuses on two fundamental topics, specifically in medical statistics: the modelling of correlated survival datasets and the variable selection of the significant covariates and random effects. In particular, two types of survival data are considered: the classical survival datasets, where subjects are likely to experience only one type of event and the competing risks datasets, where subjects are likely to experience one of several types of event. In Chapter 2, among other topics, we highlight the importance of adding frailty terms on the proposed models in order to account for the association between the survival time and characteristics of subjects/groups. The main novelty of this thesis is to simultaneously select fixed effects and frailty terms through the proposed statistical models for each survival dataset. Chapter 3 covers the analysis of the classical survival dataset through the proposed Cox Proportional Hazard (PH) model. Utilizing a Cox PH frailty model, may increase the dimension of variable components and estimation of the unknown coefficients becomes very challenging. The method proposed for the analysis of classical survival datasets involves simultaneous variable selection on both fixed effects and frailty terms through penalty functions. The benefit of penalty functions is that they identify the non-significant parameters and set them to have a zero effect in the model. Hence, the idea is to 'doubly-penalize' the partial likelihood of the Cox PH frailty model; one penalty for each term. Estimation and selection implemented through Newton-Raphson algorithms, whereas closed iterative forms for the estimation and selection of fixed effects and prediction of frailty terms were obtained. For the selection of frailty terms, penalties imposed on their variances since frailties are random effects. Based on the same idea, we further extend the simultaneous variable selection in the competing risks datasets in Chapter 4, using extended cause-specific frailty models. Two different scenarios are considered for frailty terms; in the first case we consider that frailty terms vary among different types of events (similar to the fixed effects) whereas in the second case we consider shared frailties over all the types of events. Moreover, our 'individual penalization' approach allows for one covariate to be significant for some types of events, in contrast to the frequently used 'group-penalization' where a covariate is entirely removed when it is not significant over all the events. For both proposed methods, simulation studies were conduced and showed that the proposed procedure followed for each analysis works well in simultaneously selecting and estimating significant fixed effects and frailty terms. The proposed methods are also applied to real datasets analysis; Kidney catheter infections, Diabetes Type 2 and Breast Cancer datasets. Association of the survival times and unmeasured characteristics of the subjects was studied as well as a variable selection for fixed effects and frailties implemented successfully.
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Milner, A. D. "Detecting changes in covariate effect in the Cox proportional hazards model." Thesis, University of Newcastle Upon Tyne, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239639.

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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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Andersson, Niklas. "Estimating Companies’ Survival in Financial Crisis : Using the Cox Proportional Hazards Model." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-225982.

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This master thesis is aimed towards answering the question What is the contribution from a company’s sector with regards to its survival of a financial crisis? with the sub question Can we use survival analysis on financial data to answer this?. Thus survival analysis is used to answer our main question which is seldom used on financial data. This is interesting since it will study how well survival analysis can be used on financial data at the same time as it will evaluate if all companies experiences a financial crisis in the same way. The dataset consists of all companies traded on the Swedish stock market during 2008. The results show that the survival method is very suitable the data that is used. The sector a company operated in has a significant effect. However the power is to low too give any indication of specific differences between the different sectors. Further on it is found that the group of smallest companies had much better survival than larger companies.
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Pal, Subhamoy. "An Approach to Improving Test Powers in Cox Proportional Hazards Models." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1626893233789827.

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Cheung, Tak-lun Alan, and 張德麟. "Modelling multivariate interval-censored and left-truncated survival data using proportional hazards model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29536637.

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Books on the topic "Proportional hazards(PH) 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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Oberc, Margaret. Tree structured methods for proportional hazards model. 1993.

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Brasher, Penelope Margaret Ann. Partial residuals for the Cox proportional hazards model. 1989.

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Survival Analysis Using the Proportional Hazards Model Course Notes. Sas Inst, 2003.

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Tanaka, Yoko. A proportional hazards model for informatively censored survival times. 1998.

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Furlong, Laurence W. On assessing goodness of fit in Cox's proportional hazards regression model. 1988.

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Mahmood, Sharif. Multivariate Proportional Hazards Model: An Application To The Birth Interval In Bangladesh. LAP LAMBERT Academic Publishing, 2012.

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Book chapters on the topic "Proportional hazards(PH) model"

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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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Harrell, Frank E. "Cox Proportional Hazards Regression Model." In Regression Modeling Strategies, 465–507. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3462-1_19.

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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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Harrell, Frank E. "Cox Proportional Hazards Regression Model." In Regression Modeling Strategies, 475–519. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_20.

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Collett, D. "Model checking in the proportional hazards model." In Modelling Survival Data in Medical Research, 149–98. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3115-3_5.

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Caroni, C. "Diagnostics for Cox’s Proportional Hazards Model." In Statistics for Industry and Technology, 27–38. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8206-4_3.

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Klein, John P., and Melvin L. Moeschberger. "Refinements of the Semiparametric Proportional Hazards Model." In Statistics for Biology and Health, 295–328. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/0-387-21645-6_9.

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Klein, John P., and Melvin L. Moeschberger. "Refinements of the Semiparametric Proportional Hazards Model." In Statistics for Biology and Health, 269–303. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2728-9_9.

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Moore, Dirk F. "Regression Analysis Using the Proportional Hazards Model." In Use R!, 55–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31245-3_5.

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Kübler, Jürgen. "Kernel Estimation in the Proportional Hazards Model." In Contributions to Statistics, 435–49. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-57991-2_25.

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Conference papers on the topic "Proportional hazards(PH) model"

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Xing, Xiao, Mengshan Yu, Olayinka Tehinse, Weixing Chen, and Hao Zhang. "The Effects of Pressure Fluctuations on Hydrogen Embrittlement in Pipeline Steels." In 2016 11th International Pipeline Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ipc2016-64478.

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Hydrogen embrittlement is one of the most severe steel degradation mechanisms. Using hydrogen enhanced decohesion (HEDE) and hydrogen enhanced local plasticity (HELP), we can predict if more hydrogen atoms will accumulate into the plastic zone, enhancing the hydrogen embrittlement and the crack growth rate. In the current study, a relationship has been proposed between operations of pipeline steels and hydrogen accumulation to quantify the effects of hydrogen embrittlement. The study find that hydrogen accumulation rate is proportional to stress intensity and inversely proportional to temperature; hence, higher stress intensity and lower temperature will enhance hydrogen accumulation and crack propagation. Hydrogen potential, diffusivity, hydrostatic stress near the crack tip, and the critical loading frequency have been considered in the new model to predict crack propagation rates in pipeline steels. The predicted values are compared with experimental results of X-65 steel in two near-neutral pH solutions to verify the model. This hydrogen diffusion model helps show former neglected hazard operations such as minor cycles, and offers an easier way to optimize operations that will prolong the life of pipeline steels.
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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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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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Haoqing Li, Chunyan Hou, and Jinsong Wang. "Reliability analysis of Hadoop cluster System based on proportional hazards model." In 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2016. http://dx.doi.org/10.1109/icsess.2016.7883128.

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Thamrin, Sri Astuti, Amran, Andi Kresna Jaya, Sulvirah Rahmi, and Ansariadi. "Bayesian inference for spatial parametric proportional hazards model using Spatsurv R." In STATISTICS AND ITS APPLICATIONS: Proceedings of the 2nd International Conference on Applied Statistics (ICAS II), 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4979431.

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Zhao, Xuejing, Jinxia Su, and Xiaoping Wu. "Variable selection for Cox's proportional hazards regression model based on LASSO-CDA." In International Conference on Electrical and Electronics Engineering. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/iceee140241.

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Reports on the topic "Proportional hazards(PH) model"

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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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