Academic literature on the topic 'Proportional hazards (PH)'
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Journal articles on the topic "Proportional hazards (PH)"
Xu, Ronghui. "Proportional hazards mixed models." Advances in Methodology and Statistics 1, no. 1 (January 1, 2004): 205–12. http://dx.doi.org/10.51936/lmzi2020.
Full textAsadi, 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.
Full textFarooq, 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.
Full textZHANG, 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.
Full textEqueter, 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.
Full textTelford, Claire, Shweta Takyar, Parth Joshi, Mattias Ekman, and Nick Jones. "A network meta-analysis of fulvestrant vs alternative first-line endocrine therapies for endocrine therapy-naive postmenopausal hormone receptor-positive advanced or metastatic breast cancer." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e12545-e12545. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e12545.
Full textSharma, 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.
Full textSpirko-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.
Full textTebbi, 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.
Full textKhajehpiri, Boshra, Hamid Abrishami Moghaddam, Mohamad Forouzanfar, Reza Lashgari, Jaime Ramos-Cejudo, Ricardo S. Osorio, and Babak A. Ardekani. "Survival Analysis in Cognitively Normal Subjects and in Patients with Mild Cognitive Impairment Using a Proportional Hazards Model with Extreme Gradient Boosting Regression." Journal of Alzheimer's Disease 85, no. 2 (January 18, 2022): 837–50. http://dx.doi.org/10.3233/jad-215266.
Full textDissertations / Theses on the topic "Proportional hazards (PH)"
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.
Full textDepartment 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.
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.
Full textConference papers on the topic "Proportional hazards (PH)"
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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