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ROBEST: Ensuring robustness of evidence in public health research for increased policy impact: widened use of advanced causal inference techniques

ROBEST：确保公共卫生研究证据的稳健性以增加政策影响：广泛使用先进的因果推理技术

基本信息

批准号：
MR/W021021/1
负责人：
Camille Maringe
金额：
$ 54.42万
依托单位：
London School of Hygiene & Tropical Medicine
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FW021021%2F1
关键词：
ROBEST Ensuring robustness evidence public

项目摘要

Coherent and effective public health policies rest on reliable evidence, such that researchers are able to identify, demonstrate, and raise awareness for a need for change, as well as measure the causal effect of proposed changes. Such evidence can be built upon rich electronic health records now available in many varied research fields including public health, health economics, epidemiology and clinical science. The potential of these data is enormous as it offers a valuable source of information to obtain real-world evidence to inform public health policies. Nonetheless, reliable evidence can only be obtained through widespread use of robust statistical methodology among applied researchers with interests on evaluative research. The large number of potential confounders and their possible complex relationships with the outcome makes the use of standard regression methods challenging or even impossible in some instance. Furthermore, the observational nature of such data makes any causal interpretation of the findings with conventional analytic approaches hazardous. These caveats call for specific causal inference methodology, aimed at approaching observational data with a randomised trial mindset.Alongside the growing availability of data, there has been a rapid development of statistical tools designed to further the use of observational data to answer causal questions. One of the recently developed algorithms, blending machine learning techniques with causal inference methodology, is the targeted maximum likelihood estimation (TMLE). This cutting-edge approach combines double-robust estimation and good statistical properties, enabling causal inference.Nonetheless, there is some discrepancy between the speed of methodological development and the adoption of these innovative methods among applied researchers. We identified three reasons for this misalignment: a gap in the understanding of the new methods, a lack of ready-to-use software, and the scarcity of published publications showcasing the superiority of TMLE. We aim to address these shortcomings in this proposal.We will provide applied researchers with tutorials designed to demystify complex mathematical and statistical concepts used in the latest developments of targeted machine learning estimation. Furthermore, we propose to implement the latest TMLE developments in Stata, a statistical software favoured by most applied researchers in public health, health economics, epidemiology and clinical science. We will extend the eltmle (https://github.com/migariane/eltmle) Stata command we developed, together with extensive help file, by adding new functionalities to allow robust statistical inference. Furthermore, we plan the publication of a simple yet detailed article in the Stata Journal, online tutorials and empirical applications illustrating the use of eltmle. Lastly, we will provide demonstrations of the good properties of TMLE in simulated scenarios.We will apply eltmle command to estimate how working environment causally affects cancer incidence and mortality, and to evaluate the causal effect of the type of colon cancer surgery (laparoscopy vs. open) on 30-day mortality. Our dissemination strategy will target both applied researchers and stakeholders. It includes several channels, from classical publications and conference presentations, to dissemination through online open-source tutorials and technical support using open-source tools such as GitHub, as well as early engagement with stakeholders to develop the applied studies. Furthermore, we will run a two-day workshop hosted at the London School of Hygiene and Tropical Medicine, aiming to foster a network of eltmle users.

连贯和有效的公共卫生政策依赖于可靠的证据，这样研究人员就能够确定、论证和提高对变革必要性的认识，并衡量拟议变革的因果影响。这些证据可以建立在丰富的电子健康记录基础上，现在许多不同的研究领域都可以获得，包括公共卫生、卫生经济学、流行病学和临床科学。这些数据的潜力是巨大的，因为它提供了宝贵的信息来源，以获取现实世界的证据，为公共卫生政策提供信息。尽管如此，只有在对评价研究感兴趣的应用研究人员中广泛使用可靠的统计方法，才能获得可靠的证据。大量潜在的混杂因素及其可能与结果的复杂关系使标准回归方法的使用具有挑战性，在某些情况下甚至是不可能的。此外，这些数据的观察性使得用传统分析方法对结果进行任何因果解释都是危险的。这些警告需要具体的因果推断方法，旨在以随机试验的心态接近观测数据。随着数据的日益可用，旨在进一步使用观测数据来回答因果问题的统计工具得到了迅速发展。目标最大似然估计(TMLE)是最近发展起来的一种将机器学习技术与因果推理方法相结合的算法。这一前沿方法结合了双重稳健估计和良好的统计特性，可以进行因果推理。然而，在应用研究人员中，方法学的发展速度与这些创新方法的采用之间存在一些差异。我们发现了这种错位的三个原因：对新方法的理解存在差距，缺乏现成的软件，以及缺乏展示TMLE优势的出版出版物。我们旨在解决这一提议中的这些缺点。我们将为应用研究人员提供教程，旨在揭开目标机器学习估计的最新发展中使用的复杂数学和统计概念的神秘面纱。此外，我们建议在STATA中实施TMLE的最新发展，STATA是公共卫生、卫生经济学、流行病学和临床科学中最受应用研究人员青睐的统计软件。我们将扩展我们开发的ELTMLE(https://github.com/migariane/eltmle)STATA)命令，以及大量的帮助文件，添加新的功能以实现强大的统计推断。此外，我们计划在Stata Journal上发表一篇简单但详细的文章，在线教程和经验应用程序，说明eltmle的使用。最后，我们将在模拟场景中演示TMLE的良好特性。我们将应用eltmle命令来估计工作环境对癌症发病率和死亡率的因果影响，并评估结肠癌手术类型(腹腔镜术和开腹术)对30天死亡率的因果影响。我们的传播战略将以应用研究人员和利益相关者为目标。它包括几个渠道，从经典出版物和会议演讲，到通过在线开放源码教程传播，以及使用GitHub等开放源码工具提供技术支持，以及及早与利益攸关方接触，以开发应用研究。此外，我们将在伦敦卫生和热带医学院举办为期两天的研讨会，旨在培养eltmle用户网络。