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.

Figure 1.

Distribution of SARS-CoV-2 RNA from nasopharyngeal swabs in active and placebo arms by study visit in overall cohort (A and B) and among those with vRNA ≥ LLoQ at baseline/day 0 (C and D). Levels of SARS-CoV-2 RNA (log₁₀ copies/mL) with horizontal line = median, box = interquartile range, and whiskers = minimum/maximum (A and C); results below the LLoQ are plotted using an imputed value of 1 log₁₀ copies/mL. Proportion with quantifiable vs unquantifiable SARS-CoV-2 RNA (B and D). Abbreviations: LLOQ, lower limit of quantification; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

Figure 2.

Quantile-quantile (Q-Q) plot for linear regression model for censored data for change in viral RNA from baseline to day 3 (A) and to day 7 (B); both models included an indicator variable for treatment versus placebo and adjusted for baseline viral RNA. Standardized residuals (for the noncensored observations) calculated by dividing the residuals by their standard deviation (estimated from the fitted model). Quantiles for a standard normal distribution plotted on the x-axis take account of censored residuals. Q-Q plots that show a linear association (data points falling along the diagonal line in a linear fashion) reflect that the normality assumption is reasonable. If the points depart markedly from the line, this implies the data are not normally distributed and may have outliers, are skewed (left or right), or are under- or overdispersed.