Moser Carlee B, Chew Kara W, Giganti Mark J, Li Jonathan Z, Aga Evgenia, Ritz Justin, Greninger Alexander L, Javan Arzhang Cyrus, Daar Eric S, Currier Judith S, Eron Joseph J, Smith Davey M, Hughes Michael D
Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, 02115, USA.
Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, 90024, USA.
medRxiv. 2023 Mar 17:2023.03.13.23287208. doi: 10.1101/2023.03.13.23287208.
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 <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.
大多数评估新冠病毒治疗方法的临床试验都包括对抗病毒活性的评估。在最近完成的门诊试验中，通常使用协方差分析（ANCOVA）或重复测量混合模型（MMRM）对鼻拭子中严重急性呼吸综合征冠状病毒2（SARS-CoV-2）RNA水平相对于基线的变化进行评估，对于低于检测定量下限（LLoQ）的结果采用单值插补法。使用单值插补值分析病毒RNA水平的变化可能会导致治疗效果的偏差估计。在本文中，我们以ACTIV-2试验中的一个示例为例，强调在使用ANCOVA或MMRM方法时插补的潜在陷阱，并说明在将<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数据时的最佳做法应包括有关检测方法及其LLoQ的详细信息、病毒RNA数据的完整性总结，以及基线病毒RNA≥LLoQ的参与者和病毒RNA<LLoQ的参与者的结果。
