Explanatory studies, such as randomized controlled trials, are targeted to extract the true causal effect of interventions on outcomes and are by design adjusted for covariates through randomization. On the contrary, observational studies are a representation of events that occurred without intervention. Both can be illustrated using the Structural Causal Model (SCM), and do-calculus can be employed to estimate the causal effects. Pragmatic clinical trials (PCT) fall between these two ends of the trial design spectra and are thus hard to define. Due to its pragmatic nature, no standardized representation of PCT through SCM has been yet established. In this paper, we approach this problem by proposing a generalized representation of PCT under the rubric of structural causal models (SCM). We discuss different analysis techniques commonly employed in PCT using the proposed graphical model, such as intention-to-treat, as-treated, and per-protocol analysis. To show the application of our proposed approach, we leverage an experimental dataset from a pragmatic clinical trial. Our proposition of SCM through PCT creates a pathway to leveraging do-calculus and related mathematical operations on clinical datasets.
翻译:解释性研究,如随机控制试验,旨在提取干预结果的真正因果关系,并按随机性调整共同变化的设计加以调整。相反,观察性研究是无干预性事件的一种表示。两者都可以使用结构因果关系模型(SCM)加以说明,并且可以使用实际计算法来估计因果关系。实用临床试验(PCT)介于试验设计光谱的这两个端之间,因此难以界定。由于其实用性,尚未通过SCM确定PCT的标准化代表性。在本文件中,我们通过在结构性因果关系模型(SCM)的标注下提出PCT的普遍代表性来处理这一问题。我们讨论PCT通常使用的不同分析技术,使用拟议的图形模型,例如意图-处理、经处理和按protocol分析。为了显示我们拟议方法的应用,我们利用了从实用性临床试验中得出的实验数据集。我们通过PCT提出的SCM建议为临床数据集的利用量标值和相关数学操作创造了一条途径。