Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, USA.
Department of Mental Health, The Johns Hopkins Bloomberg School of Public Health, USA.
Addict Behav. 2019 Jul;94:124-132. doi: 10.1016/j.addbeh.2018.10.033. Epub 2018 Oct 25.
Randomized trials are considered the gold standard for assessing the causal effects of a drug or intervention in a study population, and their results are often utilized in the formulation of health policy. However, there is growing concern that results from trials do not necessarily generalize well to their respective target populations, in which policies are enacted, due to substantial demographic differences between study and target populations. In trials related to substance use disorders (SUDs), especially, strict exclusion criteria make it challenging to obtain study samples that are fully "representative" of the populations that policymakers may wish to generalize their results to. In this paper, we provide an overview of post-trial statistical methods for assessing and improving upon the generalizability of a randomized trial to a well-defined target population. We then illustrate the different methods using a randomized trial related to methamphetamine dependence and a target population of substance abuse treatment seekers, and provide software to implement the methods in R using the "generalize" package. We discuss several practical considerations for researchers who wish to utilize these tools, such as the importance of acquiring population-level data to represent the target population of interest, and the challenges of data harmonization.
随机对照试验被认为是评估药物或干预措施在研究人群中因果效应的金标准，其结果通常用于制定卫生政策。然而，人们越来越担心，由于研究人群和目标人群之间存在大量的人口统计学差异，试验结果不一定能很好地推广到各自的目标人群，即政策实施的人群。在与物质使用障碍（SUD）相关的试验中，由于严格的排除标准，很难获得完全“代表”决策者可能希望将其结果推广到的人群的研究样本。在本文中，我们提供了一种概述，介绍了事后统计方法，用于评估和提高随机试验对明确定义的目标人群的可推广性。然后，我们使用与甲基苯丙胺依赖相关的随机试验和物质滥用治疗寻求者的目标人群来说明不同的方法，并提供了使用“generalize”包在 R 中实现这些方法的软件。我们讨论了希望使用这些工具的研究人员的几个实际考虑因素，例如获取代表性目标人群数据的重要性，以及数据协调的挑战。
