Results 211 to 220 of about 190,787 (244)

Survival analysis via cox proportional hazards additive models

Encyclopedia with Semantic Computing and Robotic Intelligence, 2017
The Cox proportional hazards model is commonly used to examine the covariate-adjusted association between a predictor of interest and the risk of mortality for censored survival data. However, it assumes a parametric relationship between covariates and mortality risk though a linear predictor.
Lu Bai, Daniel L. Gillen
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The Robust Inference for the Cox Proportional Hazards Model

Journal of the American Statistical Association, 1989
Abstract We derive the asymptotic distribution of the maximum partial likelihood estimator β for the vector of regression coefficients β under a possibly misspecified Cox proportional hazards model. As in the parametric setting, this estimator β converges to a well-defined constant vector β*.
D. Y. Lin, L. J. Wei
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Diagnostics for Cox’s Proportional Hazards Model

2004
Although Cox’s proportional hazards model is applied so often, diagnostic methods are not well known to non-expert users. Latest results suggest that individually scaled Schoenfeld residuals, dfbetas and added variable plots should be provided for assessing the model. Most of these methods are absent from widely used packages.
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Relating Cox's Proportional Hazard Model to the Exponential Model for Survival

Biometrical Journal, 1987
AbstractAn approximate representation is given for the partial likelihood estimate of the regression coefficient in Cox's proportional hazard model which indicates how it measures the association between survival time and covariate. The case of a single covariate is concentrated on.
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Cox Proportional Hazards Regression Model

2001
The Cox proportional hazards model 132 is the most popular model for the analysis of survival data. It is a semiparametric model; it makes a parametric assumption concerning the effect of the predictors on the hazard function, but makes no assumption regarding the nature of the hazard function λ(t) itself.
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L₁Penalized Estimation in the Cox Proportional Hazards Model

Biometrical Journal, 2010
AbstractThis article presents a novel algorithm that efficiently computesL1penalized (lasso) estimates of parameters in high‐dimensional models. The lasso has the property that it simultaneously performs variable selection and shrinkage, which makes it very useful for finding interpretable prediction rules in high‐dimensional data. The new algorithm is
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Multiple Imputation for the Cox Proportional Hazards Model with Missing Covariates

Lifetime Data Analysis, 1997
We present three multiple imputation estimates for the Cox model with missing covariates. Two of the suggested estimates are asymptotically equivalent to estimates in the literature when the number of multiple imputations approaches infinity. The third estimate can be implemented using standard software that could handle time-varying covariates.
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Using fractional polynomials and restricted cubic splines to model non‐proportional hazards or time‐varying covariate effects in the Cox regression model

Statistics in Medicine, 2022
Peter C Austin, Jiming Fang, Douglas S Lee   +2 more
exaly  

Cox Proportional Hazards Model

Journal of Clinical Nursing, 2002
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Essays on semiparametric cox proportional hazard models

2009
In this dissertation I study different versions of the semiparametric proportional hazard duration model and their practical applications under both frequentist and Bayesian econometrics frameworks. I use the unemployment spell data set that is created from the Panel Study of Income Dynamics (PSID).In Chapter 1 I study the effects of unemployment ...
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