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Linear Mixed Models - Assessing the Relationship Between a Biomarker and Cancer Disease Status
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics. Uppsala University.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Previous research suggests that a specific biomarker measured in the blood correlates with cancer status, for a specific type of cancer: higher values of the biomarker are generally found in patients with progressive cancer. The aim of this study is to investigate this relationship using a Linear mixed model. Patients with a progressive disease have on average significantly higher values of the log of the biomarker and patients with partial or complete remission of the disease have on average significantly lower values of the logged biomarker, both compared to patients with a stable disease. Also, patients with a liver tumor have on average higher values of the log of the biomarker, compared to patients without. Including both a random intercept and a random slope in the Linear mixed model, in addition to the fixed effects, results in the best model fit. Due to the non-random sample used, these results are only valid for this specific sample but can be of guidance for the conduction and planning of future studies.

Place, publisher, year, edition, pages
2018. , p. 27
Series
Inger Persson
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-353308OAI: oai:DiVA.org:uu-353308DiVA, id: diva2:1216796
Subject / course
Statistics
Educational program
Master Programme in Statistics
Supervisors
Examiners
Available from: 2018-06-19 Created: 2018-06-12 Last updated: 2018-06-19Bibliographically approved

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fulltext(418 kB)12 downloads
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • en-US
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Output format
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