EXPLORING THE MODIFIED GAMMA FRAILTY DISTRIBUTION: AN OPTIMAL DESIGN APPROACH USING PYTHON

Authors

Keywords:

Frailty Models, Censorship, Survival Analysis, Python

Abstract

A suction/injection controlled mixed convection flow of an incompressible and viscous fluid in a vertical SurvivalAnalysis is pivotal in understanding the effects of covariates on potentially censored failure times and in the joint modelling of clustered data. It is used in the context of incomplete repeated measures and failure times in longitudinal studies. Survival data are often subject to right censoring and to a subsequent loss of information about the effect of explanatory variables. Frailty models are one common approach to handle such data.Three frailty models are used to analyze bivariate time-to-event data. All approaches accommodate right censored lifetime data and account for heterogeneity in the study population. A Modified Gamma Frailty Model is compared with two existing Frailty Models. The survival-analysis was performed using the Python.The newly derived MGF was analyzed using Python which is more robust when sample size is more than forty.The MGF model performs better than the existing models in the presence of clustering. However the CGF is preferable in the absence of clusters in a given data set.

Dimensions

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Published

03-10-2023

How to Cite

EXPLORING THE MODIFIED GAMMA FRAILTY DISTRIBUTION: AN OPTIMAL DESIGN APPROACH USING PYTHON. (2023). FUDMA JOURNAL OF SCIENCES, 7(3), 304-310. https://doi.org/10.33003/fjs-2023-0703-1970

Issue

Vol. 7 No. 3 (2023): FUDMA Journal of Sciences - Vol. 7 No. 3

Section

Research Articles
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FUDMA Journal of Sciences

How to Cite

EXPLORING THE MODIFIED GAMMA FRAILTY DISTRIBUTION: AN OPTIMAL DESIGN APPROACH USING PYTHON. (2023). FUDMA JOURNAL OF SCIENCES, 7(3), 304-310. https://doi.org/10.33003/fjs-2023-0703-1970

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