Understanding ethnic differences in the comparative effectiveness of antidiabetic medications using high-dimensional propensity scores in electronic

使用电子高维倾向评分了解抗糖尿病药物比较有效性的种族差异

• 批准号: 2580594
• 金额: --
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-2580594
• 关键词: Understanding; ethnic; differences; comparative; effectiveness

IntroductionThere are enormous inequalities in rates of type 2 diabetes across ethnic groups in the UK. Population of South Asian and African descent develop diabetes earlier in life and progress more quickly to complications, such as stroke and heart disease.A potential cause of ethnic disparities in diabetes outcomes arises because therapeutic guidelines are predominantly derived from white European populations. UK guidelines currently recommend a range of second-line diabetes treatments due to clinical equipoise. It is unknown whether treatment response differs by ethnicity. Differences in the effectiveness of medications may arise from biological factors to cultural acceptability of different medications. Project descriptionIn this project I will compare the effectiveness of antidiabetic medications at preventing complications between ethnic groups with the same class of antidiabetic medication. I also plan to use patterns in prescription data to study adherence to medications and determinants of non-adherence among patients from different ethnic groups. I will then use data from the UK biobank to study genetic susceptibility and drug response to type 2 diabetes across different ethnic groups..One disadvantage of using electronic health records to study treatment affects is the inability to control which patients are assigned to which treatment groups. In clinical trials patients are often randomly assigned to treatment groups to ensure that the groups being compared are as similar. This makes it easier to isolate the effects of the treatment. In reality, there are non-random differences between treatment groups because patients and healthcare providers choose which treatment is most suitable. I will use high-dimensional propensity scoring to control for differences in the treatment groups. This methods uses an algorithm to identify potentially confounding variables and then creates comparison groups among which these variables are balanced. This make it easier to isolate the effects of the exposure (medication combined with ethnicity) on the outcome (vascular complications of diabetes).RelevanceThis project will make a valuable contribution to the evidence base for ethnic differences in the effectiveness of second-line diabetes treatment. The findings may feed into the development targeted treatment regimens that will be more effective at preventing vascular complications for type 2 diabetes patients from different ethnic groups.MRC strategy and core skillsThis studentship meets the MRC skills priority of enhancing quantitative skills. Within this remit, I will gain skills and understanding of:- Different ways of assessing the effectiveness of treatments and commonly used statistical techniques in pharmacoepidemiology.- Triangulation between different quantitative data sources, including genomic and health utilization data.- Computational skills required to managing and analyse big data.- The application of complex quantitative techniques to control for confounding in observational datasets.Keywords: diabetes, ethnicity, electronic health records, pharmacoepidemiology, high dimensional propensity scoring.

简介英国不同种族的 2 型糖尿病发病率存在巨大差异。南亚和非洲裔人群患糖尿病的时间较早，并且发展为中风和心脏病等并发症的速度更快。糖尿病结局种族差异的一个潜在原因是治疗指南主要来自欧洲白人。由于临床平衡，英国指南目前推荐了一系列二线糖尿病治疗方法。目前尚不清楚治疗反应是否因种族而异。药物有效性的差异可能源于生物因素和不同药物的文化可接受性。项目描述在这个项目中，我将比较不同种族的抗糖尿病药物在预防并发症方面与同类抗糖尿病药物的有效性。我还计划利用处方数据模式来研究不同种族患者的药物依从性和不依从性的决定因素。然后，我将使用英国生物银行的数据来研究不同种族群体对 2 型糖尿病的遗传易感性和药物反应。使用电子健康记录来研究治疗影响的一个缺点是无法控制将哪些患者分配到哪些治疗组。在临床试验中，患者通常被随机分配到治疗组，以确保所比较的组相似。这使得更容易隔离治疗的效果。事实上，治疗组之间存在非随机差异，因为患者和医疗保健提供者会选择最合适的治疗方法。我将使用高维倾向评分来控制治疗组中的差异。该方法使用算法来识别潜在的混杂变量，然后创建比较组，其中这些变量是平衡的。这使得更容易分离暴露（药物与种族相结合）对结果（糖尿病血管并发症）的影响。相关性该项目将为二线糖尿病治疗有效性的种族差异的证据基础做出有价值的贡献。研究结果可能会有助于制定有针对性的治疗方案，从而更有效地预防不同种族的 2 型糖尿病患者的血管并发症。MRC 策略和核心技能该奖学金符合 MRC 增强定量技能的技能优先级。在这一职责范围内，我将获得以下方面的技能和理解：- 评估治疗有效性的不同方法以及药物流行病学中常用的统计技术。- 不同定量数据源之间的三角测量，包括基因组和健康利用数据。- 管理和分析大数据所需的计算技能。- 应用复杂的定量技术来控制观察数据集中的混杂因素。关键词：糖尿病， 种族、电子健康记录、药物流行病学、高维倾向评分。