ABSTRACT
BACKGROUND: Risk-based quality management is a regulatory-recommended approach to manage risk in a clinical trial. A key element of this strategy is to conduct risk-based monitoring to detect potential risks to critical data and processes earlier. However, there are limited publicly available tools to perform the analytics required for this purpose. Good Statistical Monitoring is a new open-source solution developed to help address this need. METHODS: A team of statisticians, data scientists, clinicians, data managers, clinical operations, regulatory, and quality compliance staff collaborated to design Good Statistical Monitoring, an R package, to flexibly and efficiently implement end-to-end analyses of key risks. The package currently supports the mapping of clinical trial data from a variety of formats, evaluation of 12 key risk indicators, interactive visualization of analysis results, and creation of standardized reports. RESULTS: The Good Statistical Monitoring package is freely available on GitHub and empowers clinical study teams to proactively monitor key risks. It employs a modular workflow to perform risk assessments that can be customized by replacing any workflow component with a study-specific alternative. Results can be exported to other clinical systems or can be viewed as an interactive report to facilitate follow-up risk mitigation. Rigorous testing and qualification are performed as part of each release to ensure package quality. CONCLUSIONS: Good Statistical Monitoring is an open-source solution designed to enable clinical study teams to implement statistical monitoring of critical risks, as part of a comprehensive risk-based quality management strategy.
ABSTRACT
Determinants of protective immunity against SARS-CoV-2 infection require the development of well-standardized, reproducible antibody assays to be utilized in concert with clinical trials to establish correlates of risk and protection. This need has led to the appearance of a variety of neutralization assays used by different laboratories and companies. Using plasma samples from COVID-19 convalescent individuals with mild-to-moderate disease from a localized outbreak in a single region of the western US, we compared three platforms for SARS-CoV-2 neutralization: assay with live SARS-CoV-2, pseudovirus assay utilizing lentiviral (LV) and vesicular stomatitis virus (VSV) packaging, and a surrogate ELISA test. Vero, Vero E6, HEK293T cells expressing human angiotensin converting enzyme 2 (hACE2), and TZM-bl cells expressing hACE2 and transmembrane serine protease 2 (TMPRSS2) were evaluated. Live-virus and LV-pseudovirus assay with HEK293T cells showed similar geometric mean titers (GMTs) ranging 141-178, but VSV-pseudovirus assay yielded significantly higher GMT (310 95%CI 211-454; p < 0.001). Fifty percent neutralizing dilution (ND50) titers from live-virus and all pseudovirus assay readouts were highly correlated (Pearson r = 0.81-0.89). ND50 titers positively correlated with plasma concentration of IgG against SARS-CoV-2 spike and receptor binding domain (RBD) (r = 0.63-0.89), but moderately correlated with nucleoprotein IgG (r = 0.46-0.73). There was a moderate positive correlation between age and spike (Spearmans rho=0.37, p=0.02), RBD (rho=0.39, p=0.013) and nucleoprotein IgG (rho=0.45, p=0.003). ND80 showed stronger correlation with age than ND50 (ND80 rho=0.51 (p=0.001), ND50 rho=0.28 (p=0.075)). Our data demonstrate high concordance between cell-based assays with live and pseudotyped virions.
ABSTRACT
Community-level seroprevalence surveys are needed to determine the proportion of the population with previous SARS-CoV-2 infection, a necessary component of COVID-19 disease surveillance. In May, 2020, we conducted a cross-sectional seroprevalence study of IgG antibodies for nucleocapsid of SARS-CoV-2 among the residents of Blaine County, Idaho, a ski resort community with high COVID-19 attack rates in late March and Early April (2.9% for ages 18 and older). Participants were selected from volunteers who registered via a secure web link, using prestratification weighting to the population distribution by age and gender within each ZIP Code. Participants completed a survey reporting their demographics and symptoms; 88% of volunteers who were invited to participate completed data collection survey and had 10 ml of blood drawn. Serology was completed via the Abbott Architect SARS-CoV-2 IgG immunoassay. Primary analyses estimated seroprevalence and 95% credible intervals (CI) using a hierarchical Bayesian framework to account for diagnostic uncertainty. Stratified models were run by age, sex, ZIP Code, ethnicity, employment status, and a priori participant-reported COVID-19 status. Sensitivity analyses to estimate seroprevalence included base models with post-stratification for ethnicity, age, and sex, with or without adjustment for multi-participant households. IgG antibodies to the virus that causes COVID-19 were found among 22.7% (95% CI: 20.1%, 25.5%) of residents of Blaine County. Higher levels of antibodies were found among residents of the City of Ketchum 34.8% (95% CI 29.3%, 40.5%), compared to Hailey 16.8% (95%CI 13.7%, 20.3%) and Sun Valley 19.4% (95% 11.8%, 28.4%). People who self-identified as not believing they had COVID-19 had the lowest prevalence 4.8% (95% CI 2.3%, 8.2%). The range of seroprevalence after correction for potential selection bias was 21.9% to 24.2%. This study suggests more than 80% of SARS-CoV-2 infections were not reported. Although Blaine County had high levels of SARS-CoV-2 infection, the community is not yet near the herd immunity threshold.
