Results 1 to 10 of about 1,576,634 (357)
On Cox proportional hazards model performance under different sampling schemes. [PDF]
PLoS ONE, 2023 Cox's proportional hazards model (PH) is an acceptable model for survival data analysis. This work investigates PH models' performance under different efficient sampling schemes for analyzing time to event data (survival data). We will compare a modified
Hani Samawi, Lili Yu, JingJing Yin
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Quasi-linear Cox proportional hazards model with cross- L1 penalty [PDF]
BMC Medical Research Methodology, 2020 Background To accurately predict the response to treatment, we need a stable and effective risk score that can be calculated from patient characteristics.
Katsuhiro Omae, Shinto Eguchi
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Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent
Journal of Statistical Software, 2011 We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of l1 and l2 penalties (elastic net). Our algorithm fits via cyclical coordinate descent, and employs warm starts to find a solution along a ...
Noah Simon +3 more
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Limitations of using COX proportional hazards model in cardiovascular research [PDF]
Cardiovascular DiabetologyThe article by Zhao et al. titled “Associations of Triglyceride-Glucose (TyG) Index with Chest Pain Incidence and Mortality among the U.S. Population” provides valuable insights into the positive correlation between the TyG index and chest pain incidence,
Nan Jiang, Yongfa Wu, Chengjia Li
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Perspective on Weibull proportional-hazards models [PDF]
IEEE Transactions on Reliability, 1994 This note uses a paper of Elsayed & Chan (1990) to illustrate some of the advantages and some of the limitations of the proportional hazards approach. The role of proportional hazards as one of several tools for exploratory data analysis is described.
Martin Newby
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Moving beyond the Cox proportional hazards model in survival data analysis: a cervical cancer study [PDF]
BMJ Open, 2020 Objectives This study explored the prognostic factors and developed a prediction model for Chinese-American (CA) cervical cancer (CC) patients. We compared two alternative models (the restricted mean survival time (RMST) model and the proportional ...
Lixian Li +3 more
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Optimal partitioning for the proportional hazards model. [PDF]
J Appl Stat, 2022 This paper discusses methods for clustering a continuous covariate in a survival analysis model. The advantages of using a categorical covariate defined from discretizing a continuous covariate (via clustering) is (i) enhanced interpretability of the covariate's impact on survival and (ii) relaxing model assumptions that are usually required for ...
Govindarajulu U, Tarpey T.
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Two-Level Proportional Hazards Models [PDF]
Biometrics, 2002 We extend the proportional hazards model to a two-level model with a random intercept term and random coefficients. The parameters in the multilevel model are estimated by a combination of EM and Newton-Raphson algorithms. Even for samples of 50 groups, this method produces estimators of the fixed effects coefficients that are approximately unbiased ...
Jerry J. Maples +2 more
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Explained randomness in proportional hazards models [PDF]
Statistics in Medicine, 2004 A coefficient of explained randomness, analogous to explained variation but for non-linear models, was presented by Kent. The construct hinges upon the notion of Kullback-Leibler information gain. Kent and O'Quigley developed these ideas, obtaining simple, multiple and partial coefficients for the situation of proportional hazards regression.
John O’Quigley +2 more
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A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model [PDF]
Scientific Reports, 2022 There have been few investigations of cancer prognosis models based on Bayesian hierarchical models. In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects of mRNAs on the prognosis of patients with lung adenocarcinoma ...
Na Sun +5 more
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