论文标题
小型非随机研究的因果推理方法：Covid-19的方法和应用
Causal inference methods for small non-randomized studies: Methods and an application in COVID-19
论文作者
论文摘要
通常的发育周期太慢了，无法开发疫苗，诊断和治疗方法，例如正在进行的SARS-COV-2大流行。鉴于这种情况下的压力，尽管大小和设计方面存在局限性，但仍有早期临床试验的发现被过度解释。通过一项非随机开放标签研究的激励，该研究研究了羟基氯喹对Covid-19患者的疗效，我们以统一的方式描述了各种替代方法，以分析非随机研究。一种广泛使用的工具来减少治疗选择偏差的影响是所谓的倾向评分（PS）方法。倾向得分的条件使人们可以在观察到的协变量下复制随机对照试验的设计。扩展包括GOMPOUNT的方法，该方法的应用较少，尤其是在临床研究中。此外，双重强大的估计器提供了其他优势。在这里，我们在一项模拟研究中研究了基于倾向得分的方法的特性，包括在小样本设置中的三种变体，这是典型的早期试验。提供了模拟的R代码。
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.
