Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.
Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Med Decis Making. 2022 Jul;42(5):649-660. doi: 10.1177/0272989X211072858. Epub 2022 Jan 13.
Clinical care is moving from a "one size fits all" approach to a setting in which treatment decisions are based on individual treatment response, needs, preferences, and risk. Research into personalized treatment strategies aims to discover currently unknown markers that identify individuals who would benefit from treatments that are nonoptimal at the population level. Before investing in research to identify these markers, it is important to assess whether such research has the potential to generate value. Thus, this article aims to develop a framework to prioritize research into the development of new personalized treatment strategies by creating a set of measures that assess the value of personalizing care based on a set of unknown patient characteristics.
Generalizing ideas from the value of heterogeneity framework, we demonstrate 3 measures that assess the value of developing personalized treatment strategies. The first measure identifies the potential value of personalizing medicine within a given disease area. The next 2 measures highlight specific research priorities and subgroup structures that would lead to improved patient outcomes from the personalization of treatment decisions.
We graphically present the 3 measures to perform sensitivity analyses around the key drivers of value, in particular, the correlation between the individual treatment benefits across the available treatment options. We illustrate these 3 measures using a previously published decision model and discuss how they can direct research in personalized medicine.
We discuss 3 measures that form the basis of a novel framework to prioritize research into novel personalized treatment strategies. Our novel framework ensures that research targets personalized treatment strategies that have high potential to improve patient outcomes and health system efficiency.
It is important to undertake research prioritization before conducting any research that aims to discover novel methods (e.g., biomarkers) for personalizing treatment.The value of unexplained heterogeneity can highlight disease areas in which personalizing treatment can be valuable and determine key priorities within that area.These priorities can be determined under assumptions of the magnitude of the individual-level treatment effect, which we explore in sensitivity analyses.
临床护理正从“一刀切”的方法转变为基于个体治疗反应、需求、偏好和风险的治疗决策模式。个性化治疗策略的研究旨在发现目前未知的标志物，以确定那些从人群水平上非最佳治疗中受益的个体。在投入研究以确定这些标志物之前，评估此类研究是否具有潜在价值非常重要。因此，本文旨在通过创建一套基于未知患者特征评估个性化护理价值的衡量标准，制定一个框架来优先开展新的个性化治疗策略研究。
我们借鉴异质性价值框架的思想，展示了 3 种评估开发个性化治疗策略价值的方法。第一个衡量标准确定了在特定疾病领域个性化医学的潜在价值。接下来的 2 个衡量标准突出了具体的研究重点和亚组结构，这些重点和亚组结构将从治疗决策的个性化中带来改善患者结局的效果。
我们以图形方式呈现了这 3 个衡量标准，围绕价值的关键驱动因素（特别是可用治疗方案中个体治疗获益之间的相关性）进行敏感性分析。我们使用之前发表的决策模型说明了这 3 个衡量标准，并讨论了它们如何指导个性化医学的研究。
我们讨论了 3 种衡量标准，它们构成了一个新的框架的基础，用于优先开展新的个性化治疗策略研究。我们的新框架确保研究针对具有提高患者结局和卫生系统效率高潜力的个性化治疗策略。
在开展旨在发现个性化治疗新方法（例如生物标志物）的任何研究之前，进行研究优先级排序非常重要。未解释的异质性的价值可以突出个性化治疗有价值的疾病领域，并确定该领域内的关键重点。我们在敏感性分析中探讨了个体治疗效果的假设下，可以确定这些重点。
