Real-World Effectiveness of Sotrovimab for the Early Treatment of COVID-19 During SARS-CoV-2 Delta and Omicron Waves in the USA.

INTRODUCTION: Sotrovimab, a recombinant human monoclonal antibody (mAb) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had US Food and Drug Administration Emergency Use Authorization for the treatment of high-risk outpatients with mild-to-moderate coronavirus disease 2019 (COVID-19) from 26 May 2021 to 5 April 2022. Real-world clinical effectiveness of sotrovimab in reducing the risk of 30-day all-cause hospitalization and/or mortality was evaluated for the period when the prevalence of circulating SARS-CoV-2 variants changed between Delta and Omicron in the USA. METHODS: A retrospective analysis was conducted of de-identified patients diagnosed with COVID-19 between 1 September 2021 to 30 April 2022 in the FAIR Health National Private Insurance Claims database. Patients meeting high-risk criteria were divided into two cohorts: sotrovimab and not treated with a mAb ("no mAb"). All-cause hospitalizations and facility-reported mortality ≤ 30 days of diagnosis ("30-day hospitalization or mortality") were identified. Multivariable and propensity score-matched Poisson and logistic regressions were conducted to estimate the adjusted relative risk (RR) and odds of 30-day hospitalization or mortality in each cohort. RESULTS: Compared with the no mAb cohort (n = 1,514,868), the sotrovimab cohort (n = 15,633) was older and had a higher proportion of patients with high-risk conditions. In the no mAb cohort, 84,307 (5.57%) patients were hospitalized and 8167 (0.54%) deaths were identified, while in the sotrovimab cohort, 418 (2.67%) patients were hospitalized and 13 (0.08%) deaths were identified. After adjusting for potential confounders, the sotrovimab cohort had a 55% lower risk of 30-day hospitalization or mortality (RR 0.45, 95% CI 0.41-0.49) and an 85% lower risk of 30-day mortality (RR 0.15, 95% CI 0.08-0.29). Monthly, from September 2021 to April 2022, the RR reduction for 30-day hospitalization or mortality in the sotrovimab cohort was maintained, ranging from 46% to 71% compared with the no mAb cohort; the RR estimate in April 2022 was uncertain, with wide confidence intervals due to the small sample size. CONCLUSION: Sotrovimab was associated with reduced risk of 30-day all-cause hospitalization and mortality versus no mAb treatment. Clinical effectiveness persisted during Delta and early Omicron variant waves and among all high-risk subgroups assessed.

Safety and Immunogenicity of a Novel Intranasal Influenza Vaccine (NasoVAX): A Phase 2 Randomized, Controlled Trial.

Annual influenza vaccination greatly reduces morbidity and mortality, but effectiveness remains sub-optimal. Weaknesses of current vaccines include low effectiveness against mismatched strains, lack of mucosal and other effective tissue-resident immune responses, weak cellular immune responses, and insufficiently durable immune responses. The safety and immunogenicity of NasoVAX, a monovalent intranasal influenza vaccine based on a replication-deficient adenovirus type 5 platform, were evaluated in a placebo-controlled single ascending-dose study. Sixty healthy adults (18-49 years) received a single intranasal dose of 1×109 viral particles (vp), 1 × 1010 vp, or 1 × 1011 vp of NasoVAX or placebo. NasoVAX was well-tolerated and elicited robust influenza-specific systemic and mucosal immune responses. The highest NasoVAX dose and the approved Fluzone® influenza vaccine elicited comparable hemagglutination inhibition (HAI) geometric mean titers (152.8 vs. 293.4) and microneutralization (MN) geometric mean titers (142.5 vs. 162.8), with NasoVAX HAI titers maintained more than 1-year on average following a single dose. Hemagglutinin-specific T cells responses were also documented in peripheral mononuclear cell (PBMC) preparations. Consistent with the intranasal route of administration, NasoVAX elicited antigen-specific mucosal IgA responses in the nasopharyngeal cavity with an increase of approximately 2-fold over baseline GMT at the mid- and high-doses. In summary, NasoVAX appeared safe and elicited a broad immune response, including humoral, cellular, and mucosal immunity, with no impact of baseline anti-adenovirus antibody at the most immunogenic dose.

A comparison of multiple testing adjustment methods with block-correlation positively-dependent tests.

In high dimensional data analysis (such as gene expression, spatial epidemiology, or brain imaging studies), we often test thousands or more hypotheses simultaneously. As the number of tests increases, the chance of observing some statistically significant tests is very high even when all null hypotheses are true. Consequently, we could reach incorrect conclusions regarding the hypotheses. Researchers frequently use multiplicity adjustment methods to control type I error rates-primarily the family-wise error rate (FWER) or the false discovery rate (FDR)-while still desiring high statistical power. In practice, such studies may have dependent test statistics (or p-values) as tests can be dependent on each other. However, some commonly-used multiplicity adjustment methods assume independent tests. We perform a simulation study comparing several of the most common adjustment methods involved in multiple hypothesis testing, under varying degrees of block-correlation positive dependence among tests.

Incorporation of subject-level covariates in quantile normalization of miRNA data.

BACKGROUND: Most currently-used normalization methods for miRNA array data are based on methods developed for mRNA arrays despite fundamental differences between the data characteristics. The application of conventional quantile normalization can mask important expression differences by ignoring demographic and environmental factors. We present a generalization of the conventional quantile normalization method, making use of available subject-level covariates in a colorectal cancer study. RESULTS: In simulation, our weighted quantile normalization method is shown to increase statistical power by as much as 10 % when relevant subject-level covariates are available. In application to the colorectal cancer study, this increase in power is also observed, and previously-reported dysregulated miRNAs are rediscovered. CONCLUSIONS: When any subject-level covariates are available, the weighted quantile normalization method should be used over the conventional quantile normalization method.

Accounting for dependence induced by weighted KNN imputation in paired samples, motivated by a colorectal cancer study.

Missing data can arise in bioinformatics applications for a variety of reasons, and imputation methods are frequently applied to such data. We are motivated by a colorectal cancer study where miRNA expression was measured in paired tumor-normal samples of hundreds of patients, but data for many normal samples were missing due to lack of tissue availability. We compare the precision and power performance of several imputation methods, and draw attention to the statistical dependence induced by K-Nearest Neighbors (KNN) imputation. This imputation-induced dependence has not previously been addressed in the literature. We demonstrate how to account for this dependence, and show through simulation how the choice to ignore or account for this dependence affects both power and type I error rate control.