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Bayesian study on tumour burden using functional uniform priors in nonlinear mixed-effects models
Thesis (MCom)--Stellenbosch University, 2026.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2026
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| _version_ | 1867613898701537280 |
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| access_status_str | Open Access |
| author | Meyer, Mia |
| author2 | Mostert, Paul J. |
| author_browse | Meyer, Mia Mostert, Paul J. |
| author_facet | Mostert, Paul J. Meyer, Mia |
| author_sort | Meyer, Mia |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MCom)--Stellenbosch University, 2026. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/136238 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:43:27.297Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/136238 Bayesian study on tumour burden using functional uniform priors in nonlinear mixed-effects models Meyer, Mia Mostert, Paul J. Lesaffre, E. Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Thesis (MCom)--Stellenbosch University, 2026. Meyer, M. 2026. Bayesian study on tumour burden using functional uniform priors in nonlinear mixed-effects models. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/5e9e62cb-b4be-4772-b6de-989352fd4bb6 The choice of the noninformative prior for the model parameters in a Bayesian analysis of nonlinear (mixed) models has received significant attention in the literature. This thesis considers the use of a functional uniform prior (FUP) within nonlinear (mixed) models, specifically in dose-response and tumour growth inhibition (TGI) model applications. Traditional noninformative priors like uniform and the Jeffreys priors are widely used in the pharmaceutical industry; however, they can be quite informative in nature when mapping them onto a nonlinear functional space. Additionally, the Jeffreys prior depends on the full data structure being available when deriving it in the context of clinical trials. Bornkamp (2012) derived the FUP for a few nonlinear regression models, including exponential, power and hyperbolic-Emax models, but did not consider nonlinear mixed models. An extensive Bayesian simulation study is conducted to evaluate the operating characteristics of the FUP when compared with these standard traditional priors. The Bayesian simulation study is extended to mixed-e!ects models, specifically the exponential one-parameter models and the twoparameter TGI model. Finally, the performance of the FUP is explored when analysing oncology data on colorectal cancer. The FUP has the theoretical advantages of being transformation-invariant and of satisfying the likelihood principle. While the FUP approximates the Jeffreys prior, it also has the advantage of being specified prior to data collection, in contrast to the Jeffreys prior. Masters 2026-04-29T06:44:30Z 2026-04-29T06:44:30Z 2026-03 Thesis https://scholar.sun.ac.za/handle/10019.1/136238 en Stellenbosch University 116 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Meyer, Mia Bayesian study on tumour burden using functional uniform priors in nonlinear mixed-effects models |
| title | Bayesian study on tumour burden using functional uniform priors in nonlinear mixed-effects models |
| title_full | Bayesian study on tumour burden using functional uniform priors in nonlinear mixed-effects models |
| title_fullStr | Bayesian study on tumour burden using functional uniform priors in nonlinear mixed-effects models |
| title_full_unstemmed | Bayesian study on tumour burden using functional uniform priors in nonlinear mixed-effects models |
| title_short | Bayesian study on tumour burden using functional uniform priors in nonlinear mixed-effects models |
| title_sort | bayesian study on tumour burden using functional uniform priors in nonlinear mixed effects models |
| url | https://scholar.sun.ac.za/handle/10019.1/136238 |
| work_keys_str_mv | AT meyermia bayesianstudyontumourburdenusingfunctionaluniformpriorsinnonlinearmixedeffectsmodels |