Beyond estimation: broadening the dynamic treatment regime literature

超越估计：扩大动态治疗方案文献

• 批准号: RGPIN-2017-04221
• 负责人: Wallace, Michael
• 金额: $ 1.38万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635886
• 关键词: Beyond; estimation; broadening; dynamic; treatment

Dynamic treatment regimes are a statistical way of deciding what decisions to make. They originate in medicine, where tailoring treatments to individual patients - rather than prescribing the same drug to everyone with a particular disease - can lead to improved health. This is because the best treatment for two different patients may not be the same, such as older patients faring better than younger ones using a certain medication, or patients having varying degrees of tolerance to side-effects. By analyzing data we can find the best dynamic treatment regime for each patient: a set of rules that tell us the best treatment to prescribe at any given time based on an individual patient's characteristics. What's more, the potential of dynamic treatment regimes is not limited to medicine. In any situation where a sequence of decisions is made (such as steps in an engineering process, or decisions over time in a scientific experiment), we can analyze data to work out the best actions to take.

动态治疗方案是一种决定做出什么决定的统计方法。它们起源于医学，为个体患者量身定制治疗-而不是为患有特定疾病的每个人开同样的药物-可以改善健康。这是因为对两个不同病人的最佳治疗方法未必相同，例如使用某种药物时，年长病人的情况较年轻病人为佳，又或病人对副作用的耐受程度各有不同。通过分析数据，我们可以为每个患者找到最佳的动态治疗方案：一组规则，告诉我们在任何给定时间根据个体患者的特征开出最佳治疗方案。更重要的是，动态治疗方案的潜力不仅限于医学。在任何情况下，如果要做出一系列决策（例如工程过程中的步骤，或科学实验中的决策），我们都可以分析数据以制定最佳行动。