HOD2: Data driven semi-automated approaches to comparative effectiveness research using electronic health record data

常规收集的健康数据的电子存储和链接为评估药物有效性开辟了大量机会，可能为向患者提供更个性化的治疗建议铺平道路。电子健康记录和相关数据将被用来衡量药物效果的期望现在被写入了欧盟立法。然而，对这些类型数据的分析提供了误导性结果的突出例子导致了是否应该使用这些数据来解决这些问题的问题。最近，数据驱动的方法试图利用电子健康记录中可用的丰富数据，重新获取在传统分析中被忽视的信息，已被用于重新分析获得误导性结果的研究。在这些早期的例子中，数据驱动的方法已经能够检索到关于药物效果的有效结论。因此，这些方法提供了巨大的潜力来克服以前方法的局限性，并为从常规收集的数据中获得估计药物效果的半自动过程铺平了道路。我们的建议侧重于基于倾向评分的数据驱动方法。倾向得分分析是一种统计方法，在说明开了药和没有开药的患者之间的不同患者特征方面非常有用，以便在这两组人之间进行公平比较，以确定药物的效果。通过对药物处方过程的建模，倾向评分法试图识别开了药的患者和其他没有开药的患者在其他方面具有可比性，并通过比较这些患者之间的健康结果来衡量药物的效果。通常，执行分析的调查人员将选择哪些患者特征与药物处方过程相关。然而，调查人员希望包括的许多信息并不是直接从常规收集的数据中获得的；相反，有大量关于患者以前的病史的信息，这些信息可能共同与药物处方相关。从数以千计的可用测量中手动选择相关信息的任务并非易事。因此，有必要采用数据驱动的方法，选择要纳入分析的相关信息。我们的提案旨在探讨使用这些以数据为导向的方法，目的是增加似乎是“黑箱”方法的透明度，同时尽可能侧重于进程的自动化。作为这项工作的一部分，我们将开发一套可以在分析中自动生成的可视曲线图，这将使调查人员了解分析的关键内部工作原理。我们还将确定在这些分析中部署的最佳数据驱动方法。与传统的研究数据不同，常规收集的数据不是按照定期计划收集的，因此，出于分析的目的，调查人员希望包括的信息往往会从患者的记录中遗漏。因此，我们将探讨如何在上面讨论的数据驱动分析方法中最好地处理丢失的数据。我们项目的最后一个要素将是通过广泛的渠道传播我们的工作成果。我们的工作将与医学和社会科学中在学术、制药、监管和政策环境中的广泛量化研究人员相关。

Electronic storage and linkage of routinely-collected health data has opened up substantial opportunities to assess the effectiveness of medications, potentially paving the way for more personalised treatment advice to be given to patients. The expectation that electronic health records and related data will be used to measure medication effects is now written into EU legislation. However, prominent examples in which analysis of these types of data have provided misleading results have led to questions about whether these data should be used to address such questions. More recently, data-driven approaches which attempt to harness the wealth of data available in electronic health records, to recapture information which is overlooked in traditional analyses, have been used to re-analyse studies in which misleading results were obtained. In these early examples, data-driven approaches have been able to retrieve valid conclusions regarding medication effects. These methods, therefore, offer great potential to overcome the limitations of previous methods, and pave the way for a semi-automated process of obtaining estimated medication effects from routinely-collected data.Our proposal focuses on data-driven methods based on the propensity score. A propensity score analysis is a statistical approach that is very useful in accounting for differing patient characteristics between patients prescribed a medication and those who are not, in order to allow a fair comparison between those two groups to determine the effects of the medication. By modelling the process of medication prescription, propensity score methods attempt to identify patients prescribed the medication and others who are not prescribed the medication who are otherwise comparable, and measures effects of the medication by comparing health outcomes between these patients. Typically, investigators performing the analysis will select which patient characteristics are relevant to the process of medication prescription. However, much of the information that investigators would like to include is not directly available in routinely-collected data; instead there is a large amount of information about the patients' previous medical history that might collectively be relevant to the medication prescription. The task of manually selecting relevant information, from the thousands of measurements available, is not an easy one. Thus data-driven approaches which select relevant information to include in the analysis, are necessary. Our proposal aims to explore the use of these data-driven approaches, with the aim of increasing the transparency of what might appear to be a "black box" approach, while focusing on automation of the process insofar as is possible. As part of this, we will develop a suite of visual plots that can be automatically generated in the analysis, which will provide investigators with an understanding of the key inner workings of the analysis. We will also identify the optimal data-driven methods to deploy in these analyses. Unlike traditional research data, routinely-collected data is not collected according to a regular schedule, so for the purposes of analysis information that investigators would like to include can often be missing from the patient's record. We will therefore explore how best to handle missing data within the data-driven analytic approaches discussed above. The final element of our project will be to disseminate the results of our work through a broad range of channels. Our work will be relevant to a broad spectrum of quantitative researchers in medical and social science, in academic, pharmaceutical, regulatory and policy settings.