Bringing optimised COVID-19 vaccine schedules to immunocompromised populations: statistical elements and design
Abstract Bringing optimised coronavirus disease 2019 (COVID-19) vaccine schedules to immunocompromised populations (BOOST-IC) is a multi-site, adaptive platform trial designed to assess the effect of different booster vaccination schedules in the Australian immunocompromised population on the immuno...
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| Format: | Article |
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BMC
2025-07-01
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| Series: | Trials |
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| Online Access: | https://doi.org/10.1186/s13063-025-08965-w |
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| author | Michael Dymock James H. McMahon David Griffin Michelle Hagenauer Tom L. Snelling Julie A. Marsh On behalf of the BOOST-IC Investigator Team |
| author_facet | Michael Dymock James H. McMahon David Griffin Michelle Hagenauer Tom L. Snelling Julie A. Marsh On behalf of the BOOST-IC Investigator Team |
| author_sort | Michael Dymock |
| collection | DOAJ |
| description | Abstract Bringing optimised coronavirus disease 2019 (COVID-19) vaccine schedules to immunocompromised populations (BOOST-IC) is a multi-site, adaptive platform trial designed to assess the effect of different booster vaccination schedules in the Australian immunocompromised population on the immunogenicity, safety and cross-protection against COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. Participants from one of three immunocompromised subpopulations (people living with human immunodeficiency virus, solid organ transplants or haematological malignancies) are randomised to receive a one- or two-dose booster vaccination schedule using one of three COVID-19 vaccine brands (Pfizer, Moderna or Novavax) available in Australia. The primary endpoint is the SARS-CoV-2 anti-spike immunoglobulin G concentration at 28 days after the final dose of study vaccine and is modelled using a Bayesian hierarchical two-part model, anticipating that a significant proportion of responses may be below the limit of assay detection. We describe the structure and objectives of the BOOST-IC trial and how these are mathematically represented, modelled and reported, including specification of the estimands, statistical models and decision criteria for trial adaptations. This paper should be read in conjunction with the BOOST-IC study protocol. BOOST-IC was registered on 27 September 2022 with the Australian and New Zealand Clinical Trials Registry NCT05556720. |
| format | Article |
| id | doaj-art-97c324e81d1d49da84d3d62c92ce63cb |
| institution | Kabale University |
| issn | 1745-6215 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Trials |
| spelling | doaj-art-97c324e81d1d49da84d3d62c92ce63cb2025-08-20T04:03:00ZengBMCTrials1745-62152025-07-0126111210.1186/s13063-025-08965-wBringing optimised COVID-19 vaccine schedules to immunocompromised populations: statistical elements and designMichael Dymock0James H. McMahon1David Griffin2Michelle Hagenauer3Tom L. Snelling4Julie A. Marsh5On behalf of the BOOST-IC Investigator TeamWesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute AustraliaDepartment of Infectious Diseases, Alfred HospitalDepartment of Infectious Diseases, Alfred HospitalDepartment of Infectious Diseases, Alfred HospitalSydney School of Public Health, Faculty of Medicine and Health, University of SydneyWesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute AustraliaAbstract Bringing optimised coronavirus disease 2019 (COVID-19) vaccine schedules to immunocompromised populations (BOOST-IC) is a multi-site, adaptive platform trial designed to assess the effect of different booster vaccination schedules in the Australian immunocompromised population on the immunogenicity, safety and cross-protection against COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. Participants from one of three immunocompromised subpopulations (people living with human immunodeficiency virus, solid organ transplants or haematological malignancies) are randomised to receive a one- or two-dose booster vaccination schedule using one of three COVID-19 vaccine brands (Pfizer, Moderna or Novavax) available in Australia. The primary endpoint is the SARS-CoV-2 anti-spike immunoglobulin G concentration at 28 days after the final dose of study vaccine and is modelled using a Bayesian hierarchical two-part model, anticipating that a significant proportion of responses may be below the limit of assay detection. We describe the structure and objectives of the BOOST-IC trial and how these are mathematically represented, modelled and reported, including specification of the estimands, statistical models and decision criteria for trial adaptations. This paper should be read in conjunction with the BOOST-IC study protocol. BOOST-IC was registered on 27 September 2022 with the Australian and New Zealand Clinical Trials Registry NCT05556720.https://doi.org/10.1186/s13063-025-08965-wCOVID-19VaccineImmunocompromisedAdaptive trialStatistical modelBayesian |
| spellingShingle | Michael Dymock James H. McMahon David Griffin Michelle Hagenauer Tom L. Snelling Julie A. Marsh On behalf of the BOOST-IC Investigator Team Bringing optimised COVID-19 vaccine schedules to immunocompromised populations: statistical elements and design Trials COVID-19 Vaccine Immunocompromised Adaptive trial Statistical model Bayesian |
| title | Bringing optimised COVID-19 vaccine schedules to immunocompromised populations: statistical elements and design |
| title_full | Bringing optimised COVID-19 vaccine schedules to immunocompromised populations: statistical elements and design |
| title_fullStr | Bringing optimised COVID-19 vaccine schedules to immunocompromised populations: statistical elements and design |
| title_full_unstemmed | Bringing optimised COVID-19 vaccine schedules to immunocompromised populations: statistical elements and design |
| title_short | Bringing optimised COVID-19 vaccine schedules to immunocompromised populations: statistical elements and design |
| title_sort | bringing optimised covid 19 vaccine schedules to immunocompromised populations statistical elements and design |
| topic | COVID-19 Vaccine Immunocompromised Adaptive trial Statistical model Bayesian |
| url | https://doi.org/10.1186/s13063-025-08965-w |
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