Discussion
TND
Our first objective was to evaluate post-dose 2 pseudovirus neutralizing antibody titer (PsV-nAb ID50) against D614G as a correlate of protection for the COVID-19 primary endpoint in the COVE study via the SVE approach. SVE sharply increased under stochastic shifts of geometric mean PsV-nAb ID50 titer upwards, and sharply decreased under downward shifts, further supporting the PsV-nAb ID50 biomarker as a correlate of protection [While our analysis focused on a COVID-19 VE trial, the SVE framework is equally relevant and applicable to an arbitrary VE trial for a given vaccine regimen and pathogen. However, details of the vaccine regimen, pathogen, immunoassay, and most importantly the biomarker derived from the immunoassay provide critical context for specifying the relevant marker shifts or shift functions for addressing particular objectives. For example, consider one biomarker with very broad dynamic range across vaccine recipients with quantitative values for all vaccine recipients, compared to another biomarker for which 50% of vaccine recipients have marker value before the assay lower limit of detection (LOD). For the latter biomarker, applying SVE analysis for a grid of constant shifts ranging from a decrease to the 5Our second, totally distinct, objective evaluated how well SVE estimates from COVE could predict 2-dose mRNA VE against COVID-19 caused by different SARS-CoV-2 variants in the hypothetical context that a given variant had circulated in COVE. The SVE estimates were weakly concordant with empirical two-dose variant-specific VE estimates from external studies and tended to underestimate empirical VE. The relative lack of randomized, placebo-controlled trials (RCTs) providing empirical estimates of VE for this validation analysis partly explains the underestimation, with all but one of the studies being an observational test-negative design (TND), which, compared to RCTs, have been found to produce biased-upward estimates of VE against COVID-19 [Our evaluation of how well SVE estimates could predict three-dose VE against omicron COVID-19 yielded overestimates. This over-estimation cannot be explained by the lack of RCTs. One potential explanation is that omicron may have a greater average challenge dose and/or greater viral infectivity (discussed in the Cromer et al. [We draw two conclusions for the second objective, the first being the above-noted evidence against the postulated “variant-invariant CoP model.” Based on the low concordance of SVE estimates with empirical variant-specific VE estimates, our second conclusion is that the prediction modeling ventured a “bridge too far,” caused in part by the hypothetical context for the predictions, the limited accuracy and precision of variant-specific VE estimates in the TND observational studies as noted above, and variations among the TND studies. Another reason may be that our study did not directly assess a ‘variant-matched correlate’ that would associate variant-specific titer with incidence of variant-matched COVID-19, as nAb assay data against D614G may have limited ability to predict efficacy against other variants. This hypothesis will be tested in future work. In contrast, it was much easier to attain success for our first objective, given that it is based wholly on data directly observed in COVE with fewer assumptions and avoiding extrapolations. We conclude that the SVE framework is a promising new approach for predicting how VE would change under specified perturbations of an immune marker’s distribution, where prediction domain application details may determine success. One potential future application is modeling predicted improvement in VE with updated vaccine-insert sequence(s).