Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Statistical modelling to determine the influence of vaccine dose and prior Mycobacterium tuberculosis exposure on antigen-specific T cell responses
This dissertation investigates the effects of two subunit vaccines H1:IC31 and H56:IC31 as well as prior M.tb sensitization on the immune responses of three cohorts of South African adolescents and adults. The primary outcomes are frequencies of antigen-specific CD4 T cells expressing different comb...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English ENG |
| Published: |
Department of Statistical Sciences
2025
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613181876109312 |
|---|---|
| access_status_str | Open Access |
| author | Williams, Kelly |
| author2 | Little, Francesca |
| author_browse | Little, Francesca Williams, Kelly |
| author_facet | Little, Francesca Williams, Kelly |
| author_sort | Williams, Kelly |
| collection | Thesis |
| description | This dissertation investigates the effects of two subunit vaccines H1:IC31 and H56:IC31 as well as prior M.tb sensitization on the immune responses of three cohorts of South African adolescents and adults. The primary outcomes are frequencies of antigen-specific CD4 T cells expressing different combinations of immunological markers over three time points. Two M.tb antigens are investigated: Ag85B and ESAT-6. The dissertation compares the results produced by the standard procedures that would typically be employed in the immunology research community to investigate these aims with the results produced by employing a mixed effect modelling approach. Not only is it of interest to investigate whether the results agree, but also to investigate the difference in inference that one can make and whether the mixed effect modelling approach is able to provide greater insight into the data. Methods typically employed by the immunology community that are used in this thesis are non-parametric pair-wise tests and the data analysis pipelines mixture models for single-cell assays (MIMOSA) and combinatorial polyfunctionality analysis of single cells (COMPASS). For the mixed effect modelling approach, generalized linear mixed effect models with various hierarchical structures as well as latent variable models are employed. Results suggest that 5 μg of the vaccine induces the strongest immune response. The mixed effect modelling approach showed good potential in terms of depth of analysis and ease of interpretation, however many model assumptions were violated making inference difficult. The standard approaches where much more cumbersome to implement and interpret and resulted in significant multiple testing concerns. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/41193 |
| institution | University of Cape Town (South Africa) |
| language | English ENG |
| last_indexed | 2026-06-10T12:32:03.909Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/41193 Statistical modelling to determine the influence of vaccine dose and prior Mycobacterium tuberculosis exposure on antigen-specific T cell responses Williams, Kelly Little, Francesca Nemes, Elisa Gela , Anele Biostatistics This dissertation investigates the effects of two subunit vaccines H1:IC31 and H56:IC31 as well as prior M.tb sensitization on the immune responses of three cohorts of South African adolescents and adults. The primary outcomes are frequencies of antigen-specific CD4 T cells expressing different combinations of immunological markers over three time points. Two M.tb antigens are investigated: Ag85B and ESAT-6. The dissertation compares the results produced by the standard procedures that would typically be employed in the immunology research community to investigate these aims with the results produced by employing a mixed effect modelling approach. Not only is it of interest to investigate whether the results agree, but also to investigate the difference in inference that one can make and whether the mixed effect modelling approach is able to provide greater insight into the data. Methods typically employed by the immunology community that are used in this thesis are non-parametric pair-wise tests and the data analysis pipelines mixture models for single-cell assays (MIMOSA) and combinatorial polyfunctionality analysis of single cells (COMPASS). For the mixed effect modelling approach, generalized linear mixed effect models with various hierarchical structures as well as latent variable models are employed. Results suggest that 5 μg of the vaccine induces the strongest immune response. The mixed effect modelling approach showed good potential in terms of depth of analysis and ease of interpretation, however many model assumptions were violated making inference difficult. The standard approaches where much more cumbersome to implement and interpret and resulted in significant multiple testing concerns. 2025-03-17T09:09:07Z 2025-03-17T09:09:07Z 2024 2025-03-17T09:05:19Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/41193 en ENG application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | Biostatistics Williams, Kelly Statistical modelling to determine the influence of vaccine dose and prior Mycobacterium tuberculosis exposure on antigen-specific T cell responses |
| thesis_degree_str | Master's |
| title | Statistical modelling to determine the influence of vaccine dose and prior Mycobacterium tuberculosis exposure on antigen-specific T cell responses |
| title_full | Statistical modelling to determine the influence of vaccine dose and prior Mycobacterium tuberculosis exposure on antigen-specific T cell responses |
| title_fullStr | Statistical modelling to determine the influence of vaccine dose and prior Mycobacterium tuberculosis exposure on antigen-specific T cell responses |
| title_full_unstemmed | Statistical modelling to determine the influence of vaccine dose and prior Mycobacterium tuberculosis exposure on antigen-specific T cell responses |
| title_short | Statistical modelling to determine the influence of vaccine dose and prior Mycobacterium tuberculosis exposure on antigen-specific T cell responses |
| title_sort | statistical modelling to determine the influence of vaccine dose and prior mycobacterium tuberculosis exposure on antigen specific t cell responses |
| topic | Biostatistics |
| url | http://hdl.handle.net/11427/41193 |
| work_keys_str_mv | AT williamskelly statisticalmodellingtodeterminetheinfluenceofvaccinedoseandpriormycobacteriumtuberculosisexposureonantigenspecifictcellresponses |