AI Article Synopsis
- Many clinical trials for COVID-19 treatments assess antiviral effectiveness by monitoring changes in nasal SARS-CoV-2 RNA levels.
- Using methods like ANCOVA or MMRM and filling in missing data (imputation) can distort treatment effect estimates.
- The paper emphasizes best practices for data analysis, recommending the careful treatment of measurements below the lower limits of quantification (LLoQ) and full transparency regarding assay details and participant outcomes.
Article Abstract
Most clinical trials evaluating COVID-19 therapeutics include assessments of antiviral activity. In recently completed outpatient trials, changes in nasal SARS-CoV-2 RNA levels from baseline were commonly assessed using analysis of covariance (ANCOVA) or mixed models for repeated measures (MMRM) with single-imputation for results below assay lower limits of quantification (LLoQ). Analyzing changes in viral RNA levels with singly-imputed values can lead to biased estimates of treatment effects. In this paper, using an illustrative example from the ACTIV-2 trial, we highlight potential pitfalls of imputation when using ANCOVA or MMRM methods, and illustrate how these methods can be used when considering values
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055451 PMC http://dx.doi.org/10.1101/2023.03.13.23287208 DOI Listing Publication Analysis
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