Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
Contemp Clin Trials. 2020 Dec;99:106213. doi: 10.1016/j.cct.2020.106213. Epub 2020 Nov 11.
The usual development cycles are too slow for the development of vaccines, diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2 pandemic. Given the pressure in such a situation, there is a risk that findings of early clinical trials are overinterpreted despite their limitations in terms of size and design. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. A widely used tool to reduce the impact of treatment-selection bias are so-called propensity score (PS) methods. Conditioning on the propensity score allows one to replicate the design of a randomized controlled trial, conditional on observed covariates. Extensions include the g-computation approach, which is less frequently applied, in particular in clinical studies. Moreover, doubly robust estimators provide additional advantages. Here, we investigate the properties of propensity score based methods including three variations of doubly robust estimators in small sample settings, typical for early trials, in a simulation study. R code for the simulations is provided.
通常的开发周期对于像当前的 SARS-CoV-2 大流行这样的大流行中的疫苗、诊断和治疗的开发来说太慢了。鉴于这种情况下的压力，尽管早期临床试验在规模和设计方面存在局限性，但仍存在对其结果过度解释的风险。受一项针对 COVID-19 患者羟氯喹疗效的非随机开放标签研究的启发，我们以统一的方式描述了各种分析非随机研究的替代方法。一种广泛使用的工具，可以减少治疗选择偏差的影响是所谓的倾向评分（PS）方法。在倾向评分的条件下，可以根据观察到的协变量复制随机对照试验的设计。扩展包括不太常用的 g 计算方法，特别是在临床研究中。此外，双重稳健估计器提供了额外的优势。在这里，我们在模拟研究中调查了小样本情况下（早期试验中典型的情况）基于倾向评分的方法的特性，包括三种双重稳健估计器的变化。提供了用于模拟的 R 代码。
