Abstract
BackgroundThe 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.MethodsThe 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.ResultsWe 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.ConclusionsAs 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.
Highlights
The use of correlates of protection (CoPs) in vaccination trials offers significant advantages as useful clinical endpoint substitutes
Flexible models for the prentice criteria framework To evaluate the impact of the lack of fit corresponding to Prentice criterion 4, we simulated data using the Dunning regression model [26] in an ideal Correlate of protection (CoP) setting, where the treatment effect is fully explained by the surrogate as follows: P(Tj
We fitted Prentice model 4 on the simulated data using classical logit regression shown in Eq (1), the proposed non-linear model depicted in Eq (3) with a quadratic term logit(P(Tj = 1)) = μ T + βSZj + γZSj + γZ,2Sj2
Summary
The use of correlates of protection (CoPs) in vaccination trials offers significant advantages as useful clinical endpoint substitutes. 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. 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. Assessing a vaccine’s ability to induce immune responses that can effectively protect from infection and disease is key. The use of clinical endpoints to assess vaccine efficacy (VE) can be burdensome on the development, licensure, duration and effectiveness monitoring of immunisation trials. The terms ‘correlate’ and ‘surrogate’ of protection are common in the literature when referring to immunological endpoints, but are often used inconsistently, including by regulators and other prominent authorities.
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