介绍 DigiCAT:促进有原则地使用反事实分析法确定精神健康潜在活性成分的数字工具

A. Murray, Helen Wright, Hannah Casey, Yi Yang, Xinxin Zhu, Ingrid Obsuth, Marie Allitt, Dan Mirman, Patrick Errington, Josiah King
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摘要

背景 鉴于心理健康干预措施的开发和评估所面临的挑战和涉及的资源,获得有关哪些干预目标是最有前途的投资的早期证据是非常有价值的。观察性数据集为探讨这类问题提供了丰富的资源;然而,这些数据缺乏随机治疗,这意味着它们很容易受到混淆问题的影响。反事实分析是指潜在结果框架内的一系列技术,有助于解决混杂问题。通过这种方法,它们可以帮助区分可能反映心理健康真正有效成分的潜在干预目标,以及那些由于共同依赖于 "第三变量 "而仅与心理健康结果相关的干预目标。然而,反事实分析很少被用于这一目的,即使被用于健康研究,其实施方式也往往不够理想。其中一个关键原因可能是缺乏可获得的教程和包含最佳实践的软件。方法 为了帮助促进有原则地使用反事实分析,我们开发了 DigiCAT。DigiCAT 是一个开放的数字工具,由 R 和 Shiny 构建,实现了一系列反事实分析方法。该工具附有可访问的教程。该工具旨在处理真实数据,具有处理缺失数据、非二元处理效应和复杂调查设计的能力。结果 本文介绍了 DigiCAT 的开发过程,吸取了用户和生活经验专家的意见,并概述了其功能和使用实例。结论 在考虑了潜在的混杂因素后,反事实分析可以确定哪些目标仍与心理健康结果相关,从而帮助确定干预目标的优先次序。在明确指导的支持下,可访问的数字工具可能有助于促进这些技术的吸收和有原则的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Introducing DigiCAT: A digital tool to promote the principled use of counterfactual analysis for identifying potential active ingredients in mental health
Background Given the challenges and resources involved in mental health intervention development and evaluation, it is valuable to obtain early evidence on which intervention targets represent the most promising investments. Observational datasets provide a rich resource for exploring these types of questions; however, the lack of randomisation to treatments in these data means they are vulnerable to confounding issues. Counterfactual analysis refers to a family of techniques within the potential outcomes framework that can help address confounding. In doing so, they can help differentiate potential intervention targets that may reflect genuine active ingredients in mental health from those that are only associated with mental health outcomes due to their common dependence on ‘third variables’. However, counterfactual analysis is rarely used for this purpose and where it is used in health research it is often implemented in a suboptimal fashion. One key reason may be a lack of accessible tutorials and software that embeds best practices. Methods To help promote the principled use of counterfactual analysis we developed DigiCAT. DigiCAT is an open digital tool built in R and Shiny that implements a range of counterfactual analysis methods. It is accompanied by accessible tutorials. The tool has been designed to handle real data, with capabilities for missing data, non-binary treatment effects, and complex survey designs. Results The current article describes the development of DigiCAT, drawing on user and lived experience expert input and provides an overview of its features and examples of its uses. Conclusions Counterfactual analysis could help prioritise intervention targets by establishing which ones remain associated with mental health outcomes after accounting for potential confounding. Accessible digital tools supported by clear guidance may help promote the uptake and principled use of these techniques.
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