Statistical Challenges When Analyzing SARS-CoV-2 RNA Measurements Below the Assay Limit of Quantification in COVID-19 Clinical Trials

Abstract

Most clinical trials evaluating coronavirus disease 2019 (COVID-19) therapeutics include assessments of antiviral activity. In recently completed outpatient trials, changes in nasal severe acute respiratory syndrome coronavirus 2 (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 article, 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 <LLoQ as censored measurements. Best practices when analyzing quantitative viral RNA data should include details about the assay and its LLoQ, completeness summaries of viral RNA data, and outcomes among participants with baseline viral RNA ≥ LLoQ, as well as those with viral RNA < LLoQ. Clinical Trials Registration. NCT04518410.

Keywords: COVID-19; SARS-CoV-2 RNA; linear regression for censored data; randomized trial.