Establishing a Bayesian predictive survival model adjusting for random effects
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| Title: | Establishing a Bayesian predictive survival model adjusting for random effects |
|---|---|
| Authors: | Bartolucci, Alfred A.1 Albartol@uab.edu, Bae, Sejong2, Singh, Karan P.2 |
| Source: | Mathematics & Computers in Simulation. Jul2008, Vol. 78 Issue 2/3, p328-334. 7p. |
| Subjects: | Markov processes, Distribution (Probability theory), Clinical medicine, Medical experimentation on humans |
| Abstract: | Abstract: Myelodysplastic syndrome (MDS), sometimes referred to as pre-leukemia or smoldering leukemia, is a group of diseases usually characterized by failure of the bone marrow to produce enough normal blood cells. In about one-third of patients, the disease transforms into acute leukemia. In high-risk MDS, the bone marrow contains too many immature blood cells known as blasts. Patients with high-risk MDS survive for an average of 6–12 months. We have taken data from a large clinical trial and re-examined it considering the pre-leukemia as a random event in a Weibull distribution model. The issue becomes that of examining the affect of the variable on the predictive survival. We have taken the intercept, stress effect and shape parameter of the distribution to be random effects as well with realistic prior distributions based on previous shapes of the survival experience of subjects with this disease. We demonstrate how the model performs under relevant clinical conditions. The conditions are all tested using a Bayesian statistical approach allowing for the robust testing of the model parameters under various stress conditions which we introduce into the model. The convergence of the parameters to stable values are seen in trace plots which follow the convergence patterns This allows for precise estimation for determining clinical conditions under which the survival pattern will change. We give a numerical example of our results. The major platform for the theoretical development follows the Bayesian methodology and the multiple parameter Weibull model with random effects having carefully chosen hyper parameters. We have implemented the basic infrastructure for the analysis using the commercially available WinBugs software employing the Markov Chain Monte Carlo (MCMC) methodology. [Copyright &y& Elsevier] |
| Copyright of Mathematics & Computers in Simulation is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 32074786 AccessLevel: 6 PubType: Periodical PubTypeId: serialPeriodical PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Establishing a Bayesian predictive survival model adjusting for random effects – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bartolucci%2C+Alfred+A%2E%22">Bartolucci, Alfred A.</searchLink><relatesTo>1</relatesTo><i> Albartol@uab.edu</i><br /><searchLink fieldCode="AR" term="%22Bae%2C+Sejong%22">Bae, Sejong</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Singh%2C+Karan+P%2E%22">Singh, Karan P.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mathematics+%26+Computers+in+Simulation%22">Mathematics & Computers in Simulation</searchLink>. Jul2008, Vol. 78 Issue 2/3, p328-334. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+medicine%22">Clinical medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+experimentation+on+humans%22">Medical experimentation on humans</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: Myelodysplastic syndrome (MDS), sometimes referred to as pre-leukemia or smoldering leukemia, is a group of diseases usually characterized by failure of the bone marrow to produce enough normal blood cells. In about one-third of patients, the disease transforms into acute leukemia. In high-risk MDS, the bone marrow contains too many immature blood cells known as blasts. Patients with high-risk MDS survive for an average of 6–12 months. We have taken data from a large clinical trial and re-examined it considering the pre-leukemia as a random event in a Weibull distribution model. The issue becomes that of examining the affect of the variable on the predictive survival. We have taken the intercept, stress effect and shape parameter of the distribution to be random effects as well with realistic prior distributions based on previous shapes of the survival experience of subjects with this disease. We demonstrate how the model performs under relevant clinical conditions. The conditions are all tested using a Bayesian statistical approach allowing for the robust testing of the model parameters under various stress conditions which we introduce into the model. The convergence of the parameters to stable values are seen in trace plots which follow the convergence patterns This allows for precise estimation for determining clinical conditions under which the survival pattern will change. We give a numerical example of our results. The major platform for the theoretical development follows the Bayesian methodology and the multiple parameter Weibull model with random effects having carefully chosen hyper parameters. We have implemented the basic infrastructure for the analysis using the commercially available WinBugs software employing the Markov Chain Monte Carlo (MCMC) methodology. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mathematics & Computers in Simulation is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.matcom.2008.01.035 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 328 Subjects: – SubjectFull: Markov processes Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Clinical medicine Type: general – SubjectFull: Medical experimentation on humans Type: general Titles: – TitleFull: Establishing a Bayesian predictive survival model adjusting for random effects Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bartolucci, Alfred A. – PersonEntity: Name: NameFull: Bae, Sejong – PersonEntity: Name: NameFull: Singh, Karan P. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2008 Type: published Y: 2008 Identifiers: – Type: issn-print Value: 03784754 Numbering: – Type: volume Value: 78 – Type: issue Value: 2/3 Titles: – TitleFull: Mathematics & Computers in Simulation Type: main |
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