A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline Distribution
Frailty models are used in the survival analysis to account for the unobserved heterogeneity in individual risks to disease and death. To analyze the bivariate data on related survival times (e.g. matched pairs experiments, twin or family data), the shared frailty models were suggested. In this man...
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doaj-3c310df2e18c4c35a1674ea283e764772021-04-22T12:31:59ZengAustrian Statistical SocietyAustrian Journal of Statistics1026-597X2020-02-0149210.17713/ajs.v49i2.914A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline DistributionRalte Lalpawimawha0Arvind PandeyPUC, Mizoram University Frailty models are used in the survival analysis to account for the unobserved heterogeneity in individual risks to disease and death. To analyze the bivariate data on related survival times (e.g. matched pairs experiments, twin or family data), the shared frailty models were suggested. In this manuscript, we propose a new mixture shared inverse Gaussian frailty model based on modified Weibull as baseline distribution. The Bayesian approach of Markov Chain Monte Carlo technique is employed to estimate the parameters involved in the models. In addition, a simulation study is performed to compare the true values of the parameters with the estimated values. A comparison with the existing model was done by using Bayesian comparison techniques. A better model for infectious disease data related to kidney infection is suggested. http://www.ajs.or.at/index.php/ajs/article/view/914 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ralte Lalpawimawha Arvind Pandey |
spellingShingle |
Ralte Lalpawimawha Arvind Pandey A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline Distribution Austrian Journal of Statistics |
author_facet |
Ralte Lalpawimawha Arvind Pandey |
author_sort |
Ralte Lalpawimawha |
title |
A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline Distribution |
title_short |
A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline Distribution |
title_full |
A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline Distribution |
title_fullStr |
A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline Distribution |
title_full_unstemmed |
A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline Distribution |
title_sort |
mixture shared inverse gaussian frailty model under modified weibull baseline distribution |
publisher |
Austrian Statistical Society |
series |
Austrian Journal of Statistics |
issn |
1026-597X |
publishDate |
2020-02-01 |
description |
Frailty models are used in the survival analysis to account for the unobserved heterogeneity
in individual risks to disease and death. To analyze the bivariate data on related
survival times (e.g. matched pairs experiments, twin or family data), the shared frailty
models were suggested. In this manuscript, we propose a new mixture shared inverse Gaussian frailty model based on modified Weibull as baseline distribution. The Bayesian approach of Markov Chain Monte Carlo technique is employed to estimate the parameters involved in the models. In addition, a simulation study is performed to compare the true values of the parameters with the estimated values. A comparison with the existing model was done by using Bayesian comparison techniques. A better model for infectious disease data related to kidney infection is suggested.
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url |
http://www.ajs.or.at/index.php/ajs/article/view/914 |
work_keys_str_mv |
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_version_ |
1721514547978698752 |