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Kappa Model with Laplace Approximation: A Bayesian Study |
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PP: 553-564 |
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doi:10.18576/jsap/150313
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Author(s) |
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Najrullah Khan,
Md. Tanwir Akhtar,
Najia,
Mohd Shariq,
Divesh Sati,
Sourav Sharma,
Riya,
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Abstract |
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| This paper covers Kappa model in Bayesian perspective to check whether these distributions truly work well for
Prognosis for women with Breast cancer Data. The main objective is to quantitatively assess the prognostic performance of
the Kappa model for right-censored survival data using advanced Bayesian techniques. Laplace Approximation analytically
approximates the posterior distribution of parameters and Markov chain Monte Carlo (MCMC) methods simulate samples
from posterior and both approaches in the LapalcesDemon package in R. Weakly informative normal and half-Cauchy priors
are employed to ensure numerical stability and robustness of posterior estimates. The methodology will be applied to a real-
case survival problem of patients with breast cancer in relation to their tumor staining status. The performance of this model
will show that it can deliver accurate results in terms of estimating parameters using a Bayesian Kappa model, thus being a
good alternative model in survival studies for medical prognosis. |
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