HOD2: Instrumental Variable approaches for estimating heterogeneity of treatment effects to inform personalisation using electronic health records

HOD2：用于估计治疗效果异质性的工具变量方法，以使用电子健康记录为个性化提供信息

• 批准号: MR/T025212/1
• 负责人: Richard Grieve
• 金额: $ 64.68万
• 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: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT025212%2F1
• 关键词: HOD2; Instrumental; Variable; approaches; estimating

Personalised medicine aims to provide the right treatment for the right patient at the right time. The evidence to inform personalised medicine can come from patient's electronic health records. However, there are several problems with using these data to compare outcomes for groups of patients who receive alternative treatments. First, the patients in the two groups may differ according to characteristics that are not measured (for example, the patient's frailty). Second, the extent to which an intervention leads to an improvement in the patients' outcome (or harm) may differ according to these unmeasured characteristics. Third, it is often unknown which patient groups benefit from which interventions. Methods for addressing these three problems are currently unavailable. This project will develop new methods that resolve these problems and provide more accurate estimates of the effectiveness and harms of new treatments that apply to individual patients. In particular, we will develop new methods that make more realistic assumptions. Instead of assuming that we know which patient groups benefit from which treatment, we will develop approaches that can learn from the data about which subgroups benefit from which treatment. We will examine how well these new methods work in practice by testing them as part of new studies. One of these studies will examine which patients benefit from emergency surgery for common acute conditions (e.g. appendicitis), and uses data from 1.5 million hospital episodes. The other study considers which patients with type 2 Diabetes Mellitus benefit from new, more costly oral treatments, and uses prescription data from General Practice databases for 25,000 patients. Our new methods will enable us to provide more accurate, relevant evidence about which interventions work best for which patients with these two conditions. We will provide a general framework for these methods that can be applied across many different disease areas and countries. To help future studies, we will provide tutorials and guidance on using and adapting these methods in different contexts. We will run short courses and workshops to assist those designing, analysing and interpreting studies that use electronic health records to inform treatment decisions for individual patients.

个性化医疗的目的是在正确的时间为正确的患者提供正确的治疗。为个性化医疗提供信息的证据可以来自患者的电子健康记录。然而，使用这些数据来比较接受替代治疗的患者组的结果存在几个问题。首先，两组患者可能会根据未被测量的特征(例如，患者的虚弱)而有所不同。其次，根据这些不可测量的特征，干预导致患者结局(或伤害)改善的程度可能会有所不同。第三，通常不知道哪些患者群体从哪些干预措施中受益。目前还没有解决这三个问题的方法。该项目将开发解决这些问题的新方法，并对适用于个别患者的新治疗方法的有效性和危害提供更准确的估计。特别是，我们将开发做出更现实假设的新方法。我们不是假设我们知道哪些患者群体从哪种治疗中受益，而是开发能够从数据中了解哪些亚组从哪种治疗中受益的方法。作为新研究的一部分，我们将通过测试这些新方法来检查它们在实践中的效果如何。其中一项研究将检查哪些患者从常见急性疾病(如阑尾炎)的紧急手术中受益，并使用150万例医院发作的数据。另一项研究考虑了哪些2型糖尿病患者受益于新的、更昂贵的口服治疗，并使用了25,000名患者的全科医生数据库中的处方数据。我们的新方法将使我们能够提供更准确、相关的证据，说明哪种干预措施对这两种疾病的患者最有效。我们将为这些方法提供一个可应用于许多不同疾病地区和国家的总体框架。为了帮助未来的研究，我们将提供在不同环境中使用和适应这些方法的教程和指导。我们将举办短期课程和讲习班，帮助那些设计、分析和解释使用电子健康记录为个别患者的治疗决定提供信息的研究。

项目成果

How does a local instrumental variable method perform across settings with instruments of differing strengths? A simulation study and an evaluation of emergency surgery.

局部工具变量方法如何在不同强度的工具设置下执行？

• DOI: 10.1002/hec.4719
• 发表时间: 2023
• 期刊: Health economics
• 影响因子: 2.1
• 作者: Moler-Zapata S

A Machine-Learning Approach for Estimating Subgroup- and Individual-Level Treatment Effects: An Illustration Using the 65 Trial.

• DOI: 10.1177/0272989x221100717
• 发表时间: 2022-10
• 期刊: MEDICAL DECISION MAKING
• 影响因子: 3.6
• 作者: Sadique, Zia;Grieve, Richard;Diaz-Ordaz, Karla;Mouncey, Paul;Lamontagne, Francois;O'Neill, Stephen
• 通讯作者: O'Neill, Stephen

Local Instrumental Variable Methods to Address Confounding and Heterogeneity when Using Electronic Health Records: An Application to Emergency Surgery.

• DOI: 10.1177/0272989x221100799
• 发表时间: 2022-11
• 期刊: MEDICAL DECISION MAKING
• 影响因子: 3.6
• 作者: Moler-Zapata, Silvia;Grieve, Richard;Lugo-Palacios, David;Hutchings, A.;Silverwood, R.;Keele, Luke;Kircheis, Tommaso;Cromwell, David;Smart, Neil;Hinchliffe, Robert;O'Neill, Stephen
• 通讯作者: O'Neill, Stephen