A correlate of protection (CoP) is urgently needed to expedite development of additional COVID-19 vaccines to meet unprecedented global demand. To assess whether antibody titers may reasonably ...predict efficacy and serve as the basis of a CoP, we evaluated the relationship between efficacy and in vitro neutralizing and binding antibodies of 7 vaccines for which sufficient data have been generated. Once calibrated to titers of human convalescent sera reported in each study, a robust correlation was seen between neutralizing titer and efficacy (ρ = 0.79) and binding antibody titer and efficacy (ρ = 0.93), despite geographically diverse study populations subject to different forces of infection and circulating variants, and use of different endpoints, assays, convalescent sera panels and manufacturing platforms. Together with evidence from natural history studies and animal models, these results support the use of post-immunization antibody titers as the basis for establishing a correlate of protection for COVID-19 vaccines.
Beyond the primary endpoint of symptomatic COVID-19, sieve analyses that focus on SARS-CoV-2 infections will be particularly relevant to characterize the effect of viral variation on vaccine ...efficacy. Since many infections remain asymptomatic, the emphasis on symptomatic COVID-19 means that the vaccine could show excellent (trial defined) efficacy without blocking all SARS-CoV-2 infections. Current trials typically study vaccine efficacy against SARS-CoV-2 seroconversion at 3 to 6 monthly visits but can miss many infections because of waning nucleoprotein antibody detectability and limited RNA PCR nasal swab testing 6. ...it would be valuable for some vaccine efficacy trials to implement strategies to frequently test trial participants for SARS-CoV-2 infections and to sequence infections. ...frequent screening for asymptomatic infections would allow to study how the protective efficacy of the vaccine against nasal carriage or asymptomatic infection depends on SARS-CoV-2 genetics. The distribution in gray represents the expected distribution in the placebo group, while the distribution in red represents the viruses infecting vaccine participants (after vaccine sieving). Since the vaccine presents the Spike to the immune system of vaccinated individuals, the distribution of hamming distances was restricted to Spike protein sequences to focus on sites relevant to the specificity of vaccine-induced immune responses.
Threshold regression models are a diverse set of non-regular regression models that all depend on change points or thresholds. They provide a simple but elegant and interpretable way to model certain ...kinds of nonlinear relationships between the outcome and a predictor.
The R package chngpt provides both estimation and hypothesis testing functionalities for four common variants of threshold regression models. All allow for adjustment of additional covariates not subjected to thresholding. We demonstrate the consistency of the estimating procedures and the type 1 error rates of the testing procedures by Monte Carlo studies, and illustrate their practical uses using an example from the study of immune response biomarkers in the context of Mother-To-Child-Transmission of HIV-1 viruses.
chngpt makes several unique contributions to the software for threshold regression models and will make these models more accessible to practitioners interested in modeling threshold effects.
Concerns have been raised about the risk of severe dengue in children who were seronegative before receipt of a recently deployed dengue vaccine. In this study, data from field trials were analyzed ...to assess the effect of baseline serostatus on subsequent severe illness.
The rapid identification of a correlate of protection for Covid-19 vaccines — on the basis of several harmonized randomized phase 3 trials using common validated assays — constitutes an important ...success in vaccinology.
Rapid development of an efficacious vaccine against the viral pathogen severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the cause of the coronavirus disease 2019 (COVID-19) pandemic, is ...essential, but rigorous studies are required to determine the safety of candidate vaccines. Here, on behalf of the Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Working Group, we evaluate research on the potential risk of immune enhancement of disease by vaccines and viral infections, including coronavirus infections, together with emerging data about COVID-19 disease. Vaccine-associated enhanced disease has been rarely encountered with existing vaccines or viral infections. Although animal models of SARS-CoV-2 infection may elucidate mechanisms of immune protection, we need observations of enhanced disease in people receiving candidate COVID-19 vaccines to understand the risk of immune enhancement of disease. Neither principles of immunity nor preclinical studies provide a basis for prioritizing among the COVID-19 vaccine candidates with respect to safety at this time. Rigorous clinical trial design and postlicensure surveillance should provide a reliable strategy to identify adverse events, including the potential for enhanced severity of COVID-19 disease, after vaccination.
Identification of immune correlates of protection after vaccination is an important part of vaccinology for both theoretical and practical reasons. The terminology and definition of correlates have ...been confusing, because different authors have used variable terms and concepts. Here, we attempt to give precision to the field by defining 3 terms: correlate of protection (CoP), mechanistic correlate of protection (mCoP), and nonmechanistic correlate of protection (nCoP). A CoP is a marker of immune function that statistically correlates with protection after vaccination that may be either an mCoP, which is a mechanistic cause of protection, or an nCoP, which does not cause protection but nevertheless predicts protection through its (partial) correlation with another immune response(s) that mechanistically protects.
In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the ...regression technique employed. For this reason, practitioners often only resort to one of a few regression techniques for which a variable importance measure is naturally defined. Unfortunately, these regression techniques are often suboptimal for predicting the response. Additionally, because the variable importance measures native to different regression techniques generally have a different interpretation, comparisons across techniques can be difficult. In this work, we study a variable importance measure that can be used with any regression technique, and whose interpretation is agnostic to the technique used. This measure is a property of the true data‐generating mechanism. Specifically, we discuss a generalization of the analysis of variance variable importance measure and discuss how it facilitates the use of machine learning techniques to flexibly estimate the variable importance of a single feature or group of features. The importance of each feature or group of features in the data can then be described individually, using this measure. We describe how to construct an efficient estimator of this measure as well as a valid confidence interval. Through simulations, we show that our proposal has good practical operating characteristics, and we illustrate its use with data from a study of risk factors for cardiovascular disease in South Africa.
In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response-in other words, to gauge the variable ...importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection.
Supplementary materials
for this article are available online.
Failure time data subject to various types of censoring commonly arise in epidemiological and biomedical studies. Motivated by an AIDS clinical trial, we consider regression analysis of failure time ...data that include exact and left‐, interval‐, and/or right‐censored observations, which are often referred to as partly interval‐censored failure time data. We study the effects of potentially time‐dependent covariates on partly interval‐censored failure time via a class of semiparametric transformation models that includes the widely used proportional hazards model and the proportional odds model as special cases. We propose an EM algorithm for the nonparametric maximum likelihood estimation and show that it unifies some existing approaches developed for traditional right‐censored data or purely interval‐censored data. In particular, the proposed method reduces to the partial likelihood approach in the case of right‐censored data under the proportional hazards model. We establish that the resulting estimator is consistent and asymptotically normal. In addition, we investigate the proposed method via simulation studies and apply it to the motivating AIDS clinical trial.
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