Cox regression
Time-to-Event Prediction with Neural Networks and Cox Regression
... proportional Cox method and a non-proportional Cox method, which we will refer to as Cox-MLP (CC) and Cox-Time ...linear Cox regression referred to as Classical Cox ...
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The use of Cox regression and genetic algorithm (CoRGA) for identifying risk factors for mortality in older people
... e.g. regression analyses, for identifying risk factors from data gathered in longitudinal studies of older ...the Cox proportional hazard models, and from these to be selected those variables that form the ...
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Hierarchical Alpha-cut Fuzzy C-means, Fuzzy ARTMAP and Cox Regression Model for Customer Churn Prediction
... However, a few papers studied hybrid data mining techniques for customer churn prediction. Therefore, in this paper, a new hierarchical technique is presented to create the model of customer churn. This proposed ...
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A new method to analyse the pace of child development: Cox regression validated by a bootstrap resampling procedure
... The Cox regression model is employed to identify fac- tors related to loss of function and patient ...the Cox regression model the bootstrap resampling procedure is used for an investigation ...
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A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study
... dependent Cox regression methods performed best at estimating the association parameter compared to the unadjusted Cox and logistic ...the Cox models. The PLR and the time dependent Cox ...
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Comparison of Artificial Neural Networks and Cox Regression Models in Prediction of Kidney Transplant Survival
... Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard ...compare Cox regression and artificial neural network models ...
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Usage of Cox Regression Model for Forecasting of Survival Rate in Patients with the Early Stage of Non Small Cell Lung Cancer
... In the past decades a lot of investigations were focused on searching for more accurate markers of lung cancer progression. Researchers indicate that molecular markers may be useful in forecast- ing of treatment outcome ...
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Investigating the Recession Sustainability of Main Industries in Tehran Stock Exchange: Using Cox Regression
... the Cox Proportional Hazards Model to analyze stock survival ...and Cox regression is used to examine the impact of the industry type and the main macroeconomic variables affecting the Bears ...of ...
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Infant and child mortality in Indonesia : with an application of life table and Cox regression techniques to infant mortality
... and Cox regression analyses to examine the levels, trends, differentials and determinants of infant and child mortality in Indonesia, based on the 1991 and 1994 Indonesia Demographic and Health Surveys ...
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Stratified Cox Regression Analysis of Survival under CIMAvax®EGF Vaccine
... stratified Cox regression analysis to evaluate the effects of some potential prog- nostic factors on the overall survival of patients with non- small cell lung cancer, treated with the CIMAvax ® EGF ...
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Analysis of long non-coding RNAs in glioblastoma for prognosis prediction using weighted gene co-expression network analysis, Cox regression, and L1-LASSO penalization
... PRRT3-AS1) through a combination of WGCNA, univariate Cox regression analysis, and LASSO PH model. Moreover, a six-lncRNA-based risk scoring system was constructed and capable to classify GBM patients into ...
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Survival Analysis By Using Cox Regression Model With Application
... The Cox regression model (Cox, 1972) is the most popular method in regression analysis for censored survival ...maximum Cox partial ikelihood method cannot be applied directly to obtain ...
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Factors determining disease duration in Alzheimer's disease:a postmortem study of 103 cases using the Kaplan-Meier estimator and Cox regression
... The results of the Cox regression analysis for demo- graphic variables, Apo E genotype, and the neuropathologi- cal variables are shown in Table 3. The data suggest that (1) patient age had no significant ...
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Application of Cox Regression in Modeling Survival Rate of Drug Abuse
... After identifying the final preliminary model the next step was to diagnose the fit of the model. As in the case for a linear or generalized model, it was desirable to determine whether a fitted Cox ...
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Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients
... Cox regression and Kaplan Meir estimates works well in estimating the survival distribution of patients under study suffering from a particular ...estimates Cox Regression and Kaplan -Meier is ...
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Formation of translational risk score based on correlation coefficients as an alternative to Cox regression models for predicting outcome in patients with NSCLC
... Currently, Cox regression is the gold standard for prognostic modelling in cancer ...[23]. Cox regres- sion usually results in an abstract algorithm, which is optimised for prediction in a defined ...
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The use of Cox regression and genetic algorithm (CoRGA) for identifying risk factors for mortality in older people.
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Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost
... Among the iterative methods that have been largely exploited during the recent years in the statistical practice, a particular attention has been gained by the boosting: originally developed in the machine learning ...
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TIME-DEPENDENT COVARIATES IN THE COX PROPORTIONAL HAZARDS REGRESSION MODEL.pdf
... David Cox considered the introduction of predictor/explanatory variables or covariates into such ...time. Cox (2) proposed a model in which the effect of the covariates is to multiply the hazard function by ...
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Survival Analysis with Multivariate Adaptive Regression Splines using Cox-Snell Residual
... A number of distributions are known to be suitable to model survival times depending on the situations and preference by the users. The most common among these are the Exponential, Gompertz, Log- logistic, Gamma, Normal ...
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