Multivariable Mendelian randomization with incomplete measurements on the exposure variables in the Hispanic Community Health Study/Study of Latinos.

Multivariable Mendelian randomization allows simultaneous estimation of direct causal effects of multiple exposure variables on an outcome. When the exposure variables of interest are quantitative omic features, obtaining complete data can be economically and technically challenging: the measurement cost is high, and the measurement devices may have inherent detection limits. In this paper, we propose a valid and efficient method to handle unmeasured and undetectable values of the exposure variables in a one-sample multivariable Mendelian randomization analysis with individual-level data.

Efficacy of infliximab, abatacept, and cenicriviroc for the treatment of adults hospitalized with COVID-19 pneumonia.

A randomized, double-blind, placebo-controlled clinical trial was conducted to investigate the efficacy of infliximab, abatacept, and cenicriviroc in treating patients hospitalized with COVID-19. The patient's clinical status was assessed daily on an 8-point ordinal scale. We evaluated the totality of evidence on the efficacy of the 3 immunomodulators by considering all possible changes in the clinical status of each patient over time.

Impact of booster vaccination interval on SARS-CoV-2 infection, hospitalization, and death.

Objectives: We investigated how booster interval affects the risks of SARS-CoV-2 infection and Covid-19-related hospitalization and death in different age groups.

Methods: We collected data on booster receipts and Covid-19 outcomes between September 22, 2021 and February 9, 2023 for 5,769,205 North Carolina residents ≥12 years of age who had completed their primary vaccination series. We related Covid-19 outcomes to baseline characteristics and booster doses through Cox regression models.

Evaluating treatment efficacy in hospitalized COVID-19 patients, with applications to Adaptive COVID-19 Treatment Trials.

Background: The current endpoints for therapeutic trials of hospitalized COVID-19 patients capture only part of the clinical course of a patient and have limited statistical power and robustness.

Methods: We specify proportional odds models for repeated measures of clinical status, with a common odds ratio of lower severity over time. We also specify the proportional hazards model for time to each level of improvement or deterioration of clinical status, with a common hazard ratio for overall treatment benefit.

Fitting the Cox proportional hazards model to big data.

The semiparametric Cox proportional hazards model, together with the partial likelihood principle, has been widely used to study the effects of potentially time-dependent covariates on a possibly censored event time. We propose a computationally efficient method for fitting the Cox model to big data involving millions of study subjects. Specifically, we perform maximum partial likelihood estimation on a small subset of the whole data and improve the initial estimator by incorporating the remaining data through one-step estimation with estimated efficient score functions.

Proportional rates models for multivariate panel count data.

Multivariate panel count data arise when there are multiple types of recurrent events, and the observation for each study subject consists of the number of recurrent events of each type between two successive examinations. We formulate the effects of potentially time-dependent covariates on multiple types of recurrent events through proportional rates models, while leaving the dependence structures of the related recurrent events completely unspecified. We employ nonparametric maximum pseudo-likelihood estimation under the working assumptions that all types of events are independent and each type of event is a nonhomogeneous Poisson process, and we develop a simple and stable EM-type algorithm.

Mendelian randomization with incomplete measurements on the exposure in the Hispanic Community Health Study/Study of Latinos.

Mendelian randomization has been widely used to assess the causal effect of a heritable exposure variable on an outcome of interest, using genetic variants as instrumental variables. In practice, data on the exposure variable can be incomplete due to high cost of measurement and technical limits of detection. In this paper, we propose a valid and efficient method to handle both unmeasured and undetectable values of the exposure variable in one-sample Mendelian randomization analysis with individual-level data.

Effects of COVID-19 vaccination and previous SARS-CoV-2 infection on omicron infection and severe outcomes in children under 12 years of age in the USA: an observational cohort study.

Background: Data on the protection conferred by COVID-19 vaccination and previous SARS-CoV-2 infection against omicron (B.1.1.

A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data.

Mapping cell type-specific gene expression quantitative trait loci (ct-eQTLs) is a powerful way to investigate the genetic basis of complex traits. A popular method for ct-eQTL mapping is to assess the interaction between the genotype of a genetic locus and the abundance of a specific cell type using a linear model. However, this approach requires transforming RNA-seq count data, which distorts the relation between gene expression and cell type proportions and results in reduced power and/or inflated type I error.

Impact of Vaccination, Prior Infection, and Therapy on Omicron Infection and Mortality.

Background: Understanding immunity against Omicron infection and severe outcomes conferred by coronavirus disease 2019 (Covid-19) vaccination, prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and monoclonal antibody therapy will inform intervention strategies.

Methods: We considered 295 691 patients tested for SARS-CoV-2 at Cleveland Clinic between 1 October 2021 and 31 January 2022. We used logistic regression to investigate the association of vaccination and prior infection with the risk of SARS-CoV-2 infection and used Cox regression to investigate the association of vaccination, prior infection, and monoclonal antibody therapy with the risks of intensive care unit (ICU) stay and death.

Association of Primary and Booster Vaccination and Prior Infection With SARS-CoV-2 Infection and Severe COVID-19 Outcomes.

Importance: Data about the association of COVID-19 vaccination and prior SARS-CoV-2 infection with risk of SARS-CoV-2 infection and severe COVID-19 outcomes may guide prevention strategies.

Objective: To estimate the time-varying association of primary and booster COVID-19 vaccination and prior SARS-CoV-2 infection with subsequent SARS-CoV-2 infection, hospitalization, and death.

Design, Setting, And Participants: Cohort study of 10.

Mediation analysis of multiple mediators with incomplete omics data.

There is an increasing interest in using multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation, protein expressions, and metabolic profiles) to study how the relationships between phenotypes and genotypes may be mediated by other omics markers.

EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing.

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology.

Durability of Protection Against Symptomatic COVID-19 Among Participants of the mRNA-1273 SARS-CoV-2 Vaccine Trial.

This cohort study assesses the durability of protection against symptomatic COVID-19 among participants of the mRNA-1273 SARS-CoV-2 (Moderna) vaccine trial.