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Relevant bibliographies by topics / Cox proportional hazards regression

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Academic literature on the topic 'Cox proportional hazards regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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Journal articles on the topic "Cox proportional hazards regression"

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Sedgwick, P. "Cox proportional hazards regression." BMJ 347, aug09 1 (August 9, 2013): f4919. http://dx.doi.org/10.1136/bmj.f4919.

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Katz, Mitchell H., and Walter W. Hauck. "Proportional hazards (Cox) regression." Journal of General Internal Medicine 8, no. 12 (December 1993): 702–11. http://dx.doi.org/10.1007/bf02598295.

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SUDANA, I. GEDE ARI, NI LUH PUTU SUCIPTAWATI, and LUH PUTU IDA HARINI. "PENERAPAN REGRESI COX PROPORTIONAL HAZARD UNTUK MENDUGA FAKTOR-FAKTOR YANG MEMENGARUHI LAMA MENCARI KERJA." E-Jurnal Matematika 2, no. 3 (August 30, 2013): 7. http://dx.doi.org/10.24843/mtk.2013.v02.i03.p041.

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Survival analysis is a statistical method that accommodates the collection of censored data. One of popular method in survival analysis is the Cox Proportional Hazard Regression. The Cox Proportional Hazard Regression can be used to see old looking for work where data may contain censored data. This article aims investigate the characteristics of job seekers and the variables that affect old looking for work. To establish the best model using Stepwise Selection method. Prior to that the assumption of Cox Proportional Hazards Regression is tested using log minus log curve. The results obtained from Cox Proportional Hazards Regression model is as follows

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KIVELÄ, T. "Kaplan-Meier analysis and Cox proportional hazards regression." Acta Ophthalmologica 86 (September 4, 2008): 0. http://dx.doi.org/10.1111/j.1755-3768.2008.3241.x.

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Xue, Xiaonan, Xianhong Xie, and Howard D. Strickler. "A censored quantile regression approach for the analysis of time to event data." Statistical Methods in Medical Research 27, no. 3 (May 10, 2016): 955–65. http://dx.doi.org/10.1177/0962280216648724.

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The commonly used statistical model for studying time to event data, the Cox proportional hazards model, is limited by the assumption of a constant hazard ratio over time (i.e., proportionality), and the fact that it models the hazard rate rather than the survival time directly. The censored quantile regression model, defined on the quantiles of time to event, provides an alternative that is more flexible and interpretable. However, the censored quantile regression model has not been widely adopted in clinical research, due to the complexity involved in interpreting its results properly and consequently the difficulty to appreciate its advantages over the Cox proportional hazards model, as well as the absence of adequate validation procedure. In this paper, we addressed these limitations by (1) using both simulated examples and data from National Wilms’ Tumor clinical trials to illustrate proper interpretation of the censored quantile regression model and the differences and the advantages of the model compared to the Cox proportional hazards model; and (2) developing a validation procedure for the predictive censored quantile regression model. The performance of this procedure was examined using simulation studies. Overall, we recommend the use of censored quantile regression model, which permits a more sensitive analysis of time to event data together with the Cox proportional hazards model.

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Kong, Fan Hui. "Adjusting regression attenuation in the Cox proportional hazards model." Journal of Statistical Planning and Inference 79, no. 1 (June 1999): 31–44. http://dx.doi.org/10.1016/s0378-3758(98)00178-5.

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Mauger, Elizabeth A., Robert A. Wolfe, and Friedrich K. Port. "Transient effects in the cox proportional hazards regression model." Statistics in Medicine 14, no. 14 (July 30, 1995): 1553–65. http://dx.doi.org/10.1002/sim.4780141406.

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Xie, Xianhong, Howard D. Strickler, and Xiaonan Xue. "Additive Hazard Regression Models: An Application to the Natural History of Human Papillomavirus." Computational and Mathematical Methods in Medicine 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/796270.

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There are several statistical methods for time-to-event analysis, among which is the Cox proportional hazards model that is most commonly used. However, when the absolute change in risk, instead of the risk ratio, is of primary interest or when the proportional hazard assumption for the Cox proportional hazards model is violated, an additive hazard regression model may be more appropriate. In this paper, we give an overview of this approach and then apply a semiparametric as well as a nonparametric additive model to a data set from a study of the natural history of human papillomavirus (HPV) in HIV-positive and HIV-negative women. The results from the semiparametric model indicated on average an additional 14 oncogenic HPV infections per 100 woman-years related to CD4 count < 200 relative to HIV-negative women, and those from the nonparametric additive model showed an additional 40 oncogenic HPV infections per 100 women over 5 years of followup, while the estimated hazard ratio in the Cox model was 3.82. Although the Cox model can provide a better understanding of the exposure disease association, the additive model is often more useful for public health planning and intervention.

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Fisher, Lloyd D., and D. Y. Lin. "TIME-DEPENDENT COVARIATES IN THE COX PROPORTIONAL-HAZARDS REGRESSION MODEL." Annual Review of Public Health 20, no. 1 (May 1999): 145–57. http://dx.doi.org/10.1146/annurev.publhealth.20.1.145.

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Isam Alskal, Oday, and Zakariya Yahya Algamal. "Gene selection in Cox regression model based on a new adaptive penalized method." International Journal of Advanced Statistics and Probability 8, no. 1 (May 15, 2020): 16. http://dx.doi.org/10.14419/ijasp.v8i1.30566.

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The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Cox proportional hazards regression model is the most popular model in regression analysis for censored survival data. In this paper, an adaptive penalized Cox proportional hazards regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox proportional hazards regression model with the weighted least absolute shrinkage and selection operator (LASSO) method. Experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes.

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Dissertations / Theses on the topic "Cox proportional hazards regression"

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Persson, Inger. "Essays on the Assumption of Proportional Hazards in Cox Regression." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2002. http://publications.uu.se/theses/91-554-5208-6/.

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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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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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Hughes, James A. "Person, environment, and health and illness factors influencing time to first analgesia and patient experience of pain management in the adult emergency department." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/123311/3/James_Hughes_Thesis.pdf.

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This thesis explored patient, clinician, environmental and illness factors that influence how doctors and nurses treat patients who present to the emergency department in pain. The findings confirm that patients are more likely to receive analgesic medication in a shorter time and have a more positive experience with pain care when the emergency department is less busy, they have less pre-existing illness, and have a higher socioeconomic status. The identification of these factors has important implications for making changes to the way emergency departments and emergency clinicians treat pain in a timely and patient-centered manner.

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Calsavara, Vinícius Fernando. "Estimação de efeitos variantes no tempo em modelos tipo Cox via bases de Fourier e ondaletas Haar." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-26082015-140547/.

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O modelo semiparamétrico de Cox é frequentemente utilizado na modelagem de dados de sobrevivência, pois é um modelo muito flexível e permite avaliar o efeito das covariáveis sobre a taxa de falha. Uma das principais vantagens é a fácil interpretação, de modo que a razão de riscos de dois indivíduos não varia ao longo do tempo. No entanto, em algumas situações a proporcionalidade dos riscos para uma dada covariável pode não ser válida e, este caso, uma abordagem que não dependa de tal suposição é necessária. Nesta tese, propomos um modelo tipo Cox em que o efeito da covariável e a função de risco basal são representadas via bases de Fourier e ondaletas de Haar clássicas e deformadas. Propomos também um procedimento de predição da função de sobrevivência para um paciente específico. Estudos de simulações e aplicações a dados reais sugerem que nosso método pode ser uma ferramenta valiosa em situações práticas em que o efeito da covariável é dependente do tempo. Por meio destes estudos, fazemos comparações entre as duas abordagens propostas, e comparações com outra já conhecida na literatura, onde verificamos resultados satisfatórios.
The semiparametric Cox model is often considered when modeling survival data. It is very flexible, allowing for the evaluation of covariates effects. One of its main advantages is the easy of interpretation, as long as the rate of the hazards for two individuals does not vary over time. However, this proportionality of the hazards may not be true in some practical situations and, in this case, an approach not relying on such assumption is needed. In this thesis we propose a Cox-type model that allows for time-varying covariate effects, for which the baseline hazard is based on Fourier series and wavelets on a time-frequency representation. We derive a prediction method for the survival of future patients with any specific set of covariates. Simulations and an application to a real data set suggest that our method may be a valuable tool to model data in practical situations where covariate effects vary over time. Through these studies, we make comparisons between the two approaches proposed here and comparisons with other already known in the literature, where we verify satisfactory results.

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Thapa, Ram. "Modeling Mortality of Loblolly Pine Plantations." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/46726.

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Accurate prediction of mortality is an important component of forest growth and yield prediction systems, yet mortality remains one of the least understood components of the system. Whole-stand and individual-tree mortality models were developed for loblolly pine plantations throughout its geographic range in the United States. The model for predicting stand mortality were developed using stand characteristics and biophysical variables. The models were constructed using two modeling approaches. In the first approach, mortality functions for directly predicting tree number reduction were developed using algebraic difference equation method. In the second approach, a two-step modeling strategy was used where a model predicting the probability of tree death occurring over a period was developed in the first step and a function that estimates the reduction in tree number was developed in the second step. Individual-tree mortality models were developed using multilevel logistic regression and survival analysis techniques. Multilevel data structure inherent in permanent sample plots data i.e. measurement occasions nested within trees (e.g., repeated measurements) and trees nested within plots, is often ignored in modeling tree mortality in forestry applications. Multilevel mixed-effects logistic regression takes into account the full hierarchical structure of the data. Multilevel mixed-effects models gave better predictions than the fixed effects model; however, the model fits and predictions were further improved by taking into account the full hierarchical structure of the data. Semiparametric proportional hazards regression was also used to develop model for individual-tree mortality. Shared frailty model, mixed model extension of Cox proportional hazards model, was used to account for unobserved heterogeneity not explained by the observed covariates in the Cox model.
Ph. D.

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Morgan, Jerry R. "A study of promotion and attrition of mid-grade officers in the U.S. Marine Corps : are assignments a key factor? /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Mar%5FMorgan%5FJerry.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." 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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Lindberg, Erik. "A study of the effect of inbreeding in Skellefteå during the 19th century : Using Cox Proportional hazard model to analyze lifespans and Poisson/Negative Binomial regression to analyze fertility." Thesis, Umeå universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-122687.

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Inbreeding is defined as when two individuals who are related mate and produce offspring. The level of inbreeding for an individual can be determined by calculating an inbreeding coefficient. Inbreeding can enhance both positive and negative traits. The risk for recessive diseases also increase. Data from old church records from the region of Skellefteå covering individuals from the late 17th century to the early 20th century has been made available. From this data parent-child relations can be observed and levels of inbreeding calculated. By analyzing the available data using Cox Proportional Hazard regression model it was shown that the level inbreeding affected the lifespan of an individual negatively if the parents are second cousins or more closely related. Using Poisson- and Negative Binomial regression, no evicence of an effect of inbreeding of fertility could be found.

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Danardono. "Multiple Time Scales and Longitudinal Measurements in Event History Analysis." Doctoral thesis, Umeå : Dept. of Statistics, Umeå Univ, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-420.

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Books on the topic "Cox proportional hazards regression"

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O'Quigley, John. Proportional Hazards Regression. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-68639-4.

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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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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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Lunde, Asger. Th e hazards of mutual fund performance: A cox regression analysis. London: London School of Economics, Financial Markets Group, 1998.

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Persson, Inger. Essays on the Assumption of Proportional Hazards in Cox Regression. Uppsala Universitet, 2002.

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O'Quigley, John. Proportional Hazards Regression. Springer New York, 2010.

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O'Quigley, John. Survival Analysis: Proportional and Non-Proportional Hazards Regression. Springer International Publishing AG, 2022.

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O'Quigley, John. Survival Analysis: Proportional and Non-Proportional Hazards Regression. Springer, 2021.

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

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Proportional Hazards Regression (Statistics for Biology and Health). Springer, 2008.

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Book chapters on the topic "Cox proportional hazards regression"

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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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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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Broström, Göran. "Proportional Hazards and Cox Regression." In Event History Analysis with R, 43–64. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429503764-3.

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Lee, Mei-Ling Ting, G. A. Whitmore, and Bernard Rosner. "Benefits of Threshold Regression: A Case-Study Comparison with Cox Proportional Hazards Regression." In Mathematical and Statistical Models and Methods in Reliability, 359–70. Boston, MA: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-4971-5_28.

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Zhang, Mei-Jie. "Cox Proportional Hazards Regression Models for Survival Data in Cancer Research." In Biostatistical Applications in Cancer Research, 59–70. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3571-0_4.

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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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Cai, Jianwen, Donglin Zeng, and Yu Deng. "Cox-Type Proportional Hazards Models." In Methods and Applications of Statistics in Clinical Trials, 126–45. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118596333.ch9.

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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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Matthews, David Edward, and Vernon Todd Farewell. "13 Proportional Hazards Regression." In Using and Understanding Medical Statistics, 149–59. Basel: KARGER, 2007. http://dx.doi.org/10.1159/000099428.

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Kleinbaum, David G. "The Cox Proportional Hazards Model and Its Characteristics." In Survival Analysis, 83–128. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2555-1_3.

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Conference papers on the topic "Cox proportional hazards regression"

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Kuwa, Daisuke, Tadashi Dohi, and Hiroyuki Okamura. "Generalized Cox Proportional Hazards Regression-Based Software Reliability Modeling with Metrics Data." In 2013 IEEE 19th Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE, 2013. http://dx.doi.org/10.1109/prdc.2013.55.

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Peterson, Leif E. "Evolutionary algorithms applied to likelihood function maximization during poisson, logistic, and Cox proportional hazards regression analysis." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900660.

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Applebaum, Katie, Deepika Shrestha, Michael LaValley, Daniel Weiner, and Ellen Eisen. "O26-3 Metalworking fluid exposure and risk of end-stage renal disease: analyses using standard cox proportional hazards regression and competing risk analyses." In Occupational Health: Think Globally, Act Locally, EPICOH 2016, September 4–7, 2016, Barcelona, Spain. BMJ Publishing Group Ltd, 2016. http://dx.doi.org/10.1136/oemed-2016-103951.134.

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Shpagina, L. A., E. B. Logashenko, and O. S. Kotova. "ACUTE EXACERBATIONS OF OCCUPATIONAL CHRONIC OBSTRUCTIVE PULMONARY DISEASE DUE TO INDUSTRIAL AEROSOLS CONTAINING." In The 16th «OCCUPATION and HEALTH» Russian National Congress with International Participation (OHRNC-2021). FSBSI “IRIOH”, 2021. http://dx.doi.org/10.31089/978-5-6042929-2-1-2021-1-593-597.

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Abstract. Phenotypes of exacerbations of occupational chronic obstructive pulmonary disease (COPD) due to aerosols containing nanoparticles is not studied enough. The objective was to establish rate, severity, cellular type of inflammation, clinical features of acute exacerbations of occupational COPD due to industrial aerosols containing nanoparticles exposure. Materials and methods. A prospective observational study of 50 subjects with occupational COPD (of which 26 due to aerosols, containing metal nanoparticles and 24 due to aerosols containing silica nanoparticles) and of 50 subjects with COPD due to tobacco smoke performed. Follow up period was 26 (24; 30) months. Groups were matched by age, gender, COPD duration. Groups of occupational COPD has the same smoking status. Nanoparticles and dust concentrations at workplaces air were measured by inductively coupled plasma atomic emission spectrometry and by scanning electron microscopy. COPD exacerbations rate and severity, cellular type of inflammation during exacerbations were investigated. Relationships were assessed by Cox proportional-hazards regression. Results. Occupational COPD due to aerosols containing metal nanoparticles exposure was characterized by high exacerbation rate. In comparison to occupational COPD due to aerosols containing silica nanoparticles exposure the hazard ratio (HR) was 4,59, 95% CI 1,35–15,63, in relation to COPD in tobacco smokers HR was 3,35, 95% CI1,22 – 9,21. The risk of exacerbations requiring hospitalization also was higher in this group, HR 4,35, 95% CI 1,10-12,3 and HR 3,90, 95% CI 1,33–11,42, respectively. In occupational COPD due to aerosols containing silica nanoparticles the exacerbation rate was the least. Metal nanoparticles mass concentration at the workplace air was associated with COPD exacerbations HR 1,031, 95% CI 1,012–1,11, exacerbations requiring hospitalization HR 1,028, 95% CI 1,010–1,092 and with eosinophilic inflammation during COPD exacerbation ОР 0,015, 95% CI 0,002 – 0,036. Silica nanoparticles mass concentration was associated with COPD exacerbations HR 0,025, 95% CI 0,003–0,094, exacerbations requiring hospitalization HR 0,021, 95% CI 0,009–0,105 and with neutrophilic inflammation during COPD exacerbation HR 1,019, 95% CI 1,008–1,057. Exacerbations of occupational COPD due to aerosols containing nanoparticles exposure had higher rate of respiratory support and excess length of hospital stay. Conclusion. Occupational COPD exacerbations are associated with chemical composition and mass concentration of nanoparticles in industrial aerosols

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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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Park, Ji Ae, and Yu Rang Park. "VdistCox: Vertically distributed Cox proportional hazards model with hyperparameter optimization." In Pacific Symposium on Biocomputing 2023. WORLD SCIENTIFIC, 2022. http://dx.doi.org/10.1142/9789811270611_0046.

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Han, Xiaoxia, Yilong Zhang, and Yongzhao Shao. "Abstract 2409: Predictive accuracy of Cox proportional hazards cure models with application in cancer studies." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-2409.

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Han, Xiaoxia, Yilong Zhang, and Yongzhao Shao. "Abstract 2409: Predictive accuracy of Cox proportional hazards cure models with application in cancer studies." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-2409.

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Yu, Jianxiong, Eddie Y. J. Chou, and Jyh-Tyng Yau. "Estimation of the Effects of Influential Factors on Pavement Service Life with Cox Proportional Hazards Method." In Airfield and Highway Pavements Specialty Conference 2006. Reston, VA: American Society of Civil Engineers, 2006. http://dx.doi.org/10.1061/40838(191)81.

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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 "Cox proportional hazards regression"

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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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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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