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Showing 1 to 5 of 5 for “"Baseline hazard function"”.
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A comparison of some methods of modeling baseline hazard function in discrete survival models
The baseline parameter vector in a discrete-time survival model is determined by the number of time points. The larger the number of the time points, the higher the dimension of the baseline parameter vector which often leads to biased maximum likelihood estimates. One of the ways to overcome this …
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Survival Model and Estimation for Lung Cancer Patients.
… analysis, we assume an exponential form for the baseline hazard function and combine Cox proportional hazard regression for the survival study of a group of lung cancer patients. The covariates in the hazard function are estimated by maximum likelihood estimation following the proportional …
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Cox-type model validation with recurrent event data
… types of data is models for the distribution function of the time between events occurrences, especially in the presence of covariates that play a major role in having a better understanding of time to events.</p> <p>This work pertains to statistical inference of the regression parameter and …
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Confidence bands for survival curves using model assisted cox regression
… confidence bands (SCBs) for survival functions from right censored data. The approach is based on an extension of semiparametric random censorship models (SRCMs) to Cox regression, which produces reliable and more informative SCBs. SRCMs derive their rationale from their ability to …
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Nonparametric Survival Analysis under Shape Restrictions
… resort to nonparametric methods for estimating a function. Although other nonparametric approaches, such as Kaplan-Meier, kernel-based, and roughness penalty methods, are popular tools for solving function estimation problems, they suffer from some non-trivial issues like the loss of some …