Center for Biostatistics in AIDS Research, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.
Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
J Infect Dis. 2023 Aug 31;228(Suppl 2):S101-S110. doi: 10.1093/infdis/jiad285.
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.
大多数评估 2019 年冠状病毒病（COVID-19）治疗方法的临床试验都包括抗病毒活性评估。在最近完成的门诊试验中，通常使用协方差分析（ANCOVA）或重复测量的混合模型（MMRM），对从基线开始的鼻严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）RNA 水平的变化进行分析，对于低于分析定量下限（LLOQ）的结果采用单一插补法。用单值插补分析病毒 RNA 水平的变化可能会导致治疗效果的估计值产生偏差。在本文中，我们使用 ACTIV-2 试验的一个说明性示例，强调了在使用 ANCOVA 或 MMRM 方法时插补的潜在缺陷，并说明了在将 <LLOQ 的值视为删失测量值时如何使用这些方法。分析定量病毒 RNA 数据的最佳实践应包括有关检测及其 LLOQ 的详细信息、病毒 RNA 数据的完整性摘要，以及基线病毒 RNA≥LLOQ 参与者以及病毒 RNA<LLOQ 参与者的结果。临床试验注册。NCT04518410。
