Assessing correlates of protection in vaccine trials: statistical solutions in the context of high vaccine efficacy
| Autor: | Fabian Tibaldi, Andrea Callegaro |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Epidemiology
Computer science Health Informatics Context (language use) Protective Agents Statistical power High vaccine efficacy 03 medical and health sciences Immunogenicity Vaccine 0302 clinical medicine Vaccine clinical trial Outcome Assessment Health Care Clinical endpoint Rare events Humans Computer Simulation 030212 general & internal medicine Clinical Trials as Topic Vaccines lcsh:R5-920 Surrogate endpoint 030503 health policy & services Vaccination Models Immunological Correlate of protection Vaccine efficacy Risk analysis (engineering) lcsh:Medicine (General) 0305 other medical science Algorithms Research Article |
| Zdroj: | BMC Medical Research Methodology, Vol 19, Iss 1, Pp 1-7 (2019) BMC Medical Research Methodology |
| ISSN: | 1471-2288 |
| DOI: | 10.1186/s12874-019-0687-y |
| Popis: | Background The use of correlates of protection (CoPs) in vaccination trials offers significant advantages as useful clinical endpoint substitutes. Vaccines with very high vaccine efficacy (VE) are documented in the literature (VE ≥95%). The rare events (number of infections) observed in the vaccinated groups of these trials posed challenges when applying conventionally-used statistical methods for CoP assessment. In this paper, we describe the nature of these challenges, and propose easy-to-implement and uniquely-tailored statistical solutions for the assessment of CoPs in the specific context of high VE. Methods The Prentice criteria and meta-analytic frameworks are standard statistical methods for assessing vaccine CoPs, but can be problematic in high VE cases due to the rare events data available. As a result, lack of fit and the problem of infinite estimates may arise, in the former and latter methods respectively. The use of flexible models within the Prentice framework, and penalized-likelihood methods to solve the issue of infinite estimates can improve the performance of both methods in high VE settings. Results We have 1) devised flexible non-linear models to counteract the Prentice framework lack of fit, providing sufficient statistical power to the method, and 2) proposed the use of penalised likelihood approaches to make the meta-analytic framework applicable on randomized subgroups, such as regions. The performance of the proposed methods for high VE cases was evaluated by running simulations. Conclusions As vaccines with high efficacy are documented in the literature, there is a need to identify effective statistical solutions to assess CoPs. Our proposed adaptations are straight-forward and improve the performance of conventional statistical methods for high VE data, leading to more reliable CoP assessments in the context of high VE settings. |
| Databáze: | OpenAIRE |
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